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Collective Estimation of Multiple Bivariate Density Functions with Application to Angular-sampling-based Protein Structure Prediction

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B1

In this talk we develop a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs. Each log density function in the collection is modeled as a linear combination of a common set of basis functions. The shared basis functions are modeled as bivariate splines on triangulations and are estimated using data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles.

Disease Risk Estimation by Combining Case-Control Data with Aggregated Information on the Population at Risk

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B1 L4 R4102

We propose a novel statistical framework by supplementing case–control data with summary statistics on the population at risk for a subset of risk factors. Our approach is to first form two unbiased estimating equations, one based on the case–control data and the other on both the case data and the summary statistics, and then optimally combine them to derive another estimating equation to be used for the estimation.

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

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B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

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B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Statistical models based on stochastic partial differential equations

Prof. David Bolin, Statistics, KAUST

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B9 L2 H1 R2322

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

Multivariate Functional Data Analysis and Visualization

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B3 L5 R5220

As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Finding the Face in the Crowd

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An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

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Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Brain waves in boxes

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A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

2015 Workshop on Computational Space-Time Statistics

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Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

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The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Feeding in the moonlight

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Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

Postdoc Luigi Lombardo gets an Outstanding Student Poster and PICO EGU Award. Congratulations Luigi!

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The 2016 Outstanding Student Poster and PICO (OSPP) Award is awarded to Luigi Lombardo for the poster/PICO entitled Landslide triggering-thickness susceptibility, a simple proxy for landslide hazard? A test in the Mili catchment (North-Eastern Sicily, Italy). Regarding his work, Luigi said: " In the PICO presentation project we modeled the thickness at the crown of each landslide as the dependent variable. As a result, the predictive maps carried more information than classic susceptibility ones, enabling a better proxy to hazard prediction." You can find Luigi's poster here.

A clearer view of rainfall patterns

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Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

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By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

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A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

2016 Workshop Statistics for High-Dimensional & Complex Data

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Workshop on Statistics for High-Dimensional and Complex Data, Nov. 6-9, 2016​ Organizers : Prof. Marc G. Genton, Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. David Bolin, Chalmers University of Technology, Sweden Prof. Probal Chaudhuri, Indian Statistical Institute, India Prof. Dan Cooley, Colorado State University, USA​ Prof. Subhajit Dutta, IIT Kanpur, India Prof. Amanda Hering, Baylor University, USA Dr. Anna Kiriliouk, Université

Getting the jump on industrial failures

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A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Keeping antennas at peak performance

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Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

A safe sweep of weather data

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A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Improving connections for spatial analysis

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A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Smart healthcare uses stats to spot a stumble

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Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Going to extremes to predict natural disasters

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A systematic approach to selecting and configuring statistical models improves predictions of extreme events.

Letting the data speak for itself

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A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Assistant Professor Ying Sun wins American Statistical Association award

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​​​​Ying Sun, KAUST assistant professor of applied ​mathematics and computational science​, won the 2017 Section on Statistics and the Environment (ENVR) Early Investigator Award from the American Statistical Association (ASA). Sun received her award at the 2017 Joint Statistical Meetings (JSM), held from July 29 to August 3 in Baltimore, Maryland, U.S. Sun's award recognizes her important contributions to the field of environmental statistics that help to address major problems like climate change and environmental pollution. At KAUST, Sun leads the Environmental Statistics Group in the

Professor Sun receives the Environment Early Investigator Award 2017

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Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Dataset size counts for better predictions

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A statistical technique for modeling large datasets improves interpretation of climate and environmental data.

2017 Workshop Modern Statistics for Complex Data Structures

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2017 Workshop Modern Statistics for Complex Data Structures

Diagnosing the impacts of health policy

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Study design allows more rigorous evaluation of complex healthcare interventions.

One bad apple can spoil a robot swarm

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Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Modeling where the wind blows

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A new statistical technique could be used to more accurately locate global wind resources.

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

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Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

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Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.

PhD student Sabrina Vettori selected for an ENAR Distinguished Student Paper Award. Congrats!

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PhD student Sabrina Vettori has been selected by the International Biometric Society Eastern North American Region (ENAR) to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting in Atlanta ( https://www.enar.org/meetings/spring2018/index.cfm ) for her manuscript entitled " Bayesian Hierarchical Modelling of Air Pollution Extremes Using Multivariate Max-Stable Processes ". She will present her work at an oral contributed paper session, be invited to attend the ENAR President’s reception and publicly receive their award at the beginning of the ENAR President’s Invited

Revealing hidden relationships in data

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A method to visualize hidden statistical structure helps make sense of environmental data.

Making data more accessible

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Ying Sun is a multi-award-winning statistician who is inspired by the value of statistics in solving real-world problems.

Pulling rank on spatial statistics

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A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

Challenging statistics of weather extremes

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More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Zooming in on brain function

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Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Ph.D. Student Yuxiao Li won the ENAR Distinguished Student Paper Award

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KAUST Ph.D. student Yuxiao Li has been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Ph.D. Student Yuxiao Li won the ENVR student paper award

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KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Second year Ph.D. student to receive top statistics award

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KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

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Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Connecting the dots to unlock brain structure

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Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

How extreme weather exacerbates air pollution

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Simultaneously modeling of air pollutants and weather under extreme conditions highlights the potential for serious health risks.

Searching for wind for the future

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The first quantification of wind energy in Saudi Arabia points to high wind power potential for many decades to come.

Reining in computational complexity

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A more efficient approach to modeling spatial data involving thousands of variables keeps computation time in check.

Solving real-world problems

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A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

2018 Workshop on Statistics and Data Science

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The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

Stimulating deeper insights into brain function

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Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

New stats apps show a virtual reality

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Harnessing the power of virtual reality will help to visualize data and improve statistical models.

Raphaël Huser’s article among the top downloaded articles in Springer’s Environmental Sciences Journals

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Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.

Students Peng Zhong and Rustam Bekishev get their MS degree

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On December 14, 2018, KAUST welcomed 243 graduates at the University’s ninth Commencement ceremony. Among them, students Rustam Bekishev and Peng Zhong, who is now a Ph.D. student In Statistics in our group, got their MS degree. Congratulations!

Postdoc Carolina Euan received Sylvia Esterby Presentation Award from TIES

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Carolina Euán, a postdoctoral fellow in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) at the 28th Annual TIES Conference 2018 held from July 16 to 21, 2018, in Guanajuato, Mexico. Euán won the award for her talk entitled "Bernoulli Vector Autoregressive Model with Applications to Spatio-temporal Drought Events in Mexico."

Clustering helps unlock secrets of the human brain

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The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.

Simple statistics can be good enough

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Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Taking aim at outliers

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Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Epilepsy study shows the shape of things to come

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Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

Honing in on hotspots in spatial data

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More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

3 Faculty Positions in Statistics 2020 at KAUST

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The 3 faculty positions are in the Statistics Program (http://stat.kaust.edu.sa) within the Computer, Electrical, and Mathematical Sciences and Engineering Division. Currently, the Statistics Program has 7 core faculty and 10 affiliated faculty. We are primarily interested in applicants with strong background in one of the following areas: (1) Statistical Data Science and AI, including network data analysis and high-dimensional statistics (https://apply.interfolio.com/69165); (2) Statistical Climatology, with expertise in statistical analysis of climate model output data, in particular regional climate models, and in physical systems (https://apply.interfolio.com/69167); (3) Statistics for Public Health, including smart health data analysis, personalized medicine, and disease mapping (https://apply.interfolio.com/69168).

Marc Genton received an ISI Service Award 2019

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Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Shaking up the landslide model

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Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

2019 Statistics and Data Science Workshop

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From November 18 to 20 the KAUST CEMSE Division has hosted the 2019 Statistics and Data Science Workshop. The workshop, organized by Workshop Chair Professor Ying Sun from the KAUST Environmental Statistics Group, featured 17 keynote talks from eminent data science researchers from Asia, Europe, North America and on-campus. Talks included: personalized recommendation by learning from heterogeneous graph data; dynamic spatial panel models; networks, common shocks, and sequential exogeneity; unbiased Markov chain Monte Carlo, plus many more.

Testing barley's salt tolerance is a numbers game

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Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.

A second wind for turbine design

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Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms.

Training agents to walk with purpose

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By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

A sharper view of flood risk

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Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

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High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

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Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

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The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

Deep learning in the emergency department

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Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Modeling the neighborhood boosts landslide prediction

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A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

KAUST STAT students come together for second ASA chapter meeting

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Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Modeling rainfall drop by drop

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A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Getting the inside track on street design

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Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

A brainwave from social networks

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The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Predicting future hotspots in the Red Sea

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Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

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The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Beating the curse of dimensionality

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By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

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Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Heatwave model shows it is hotter in more places, more often

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New modeling shows that heatwaves across Europe have increased in both frequency and spatial extent over the past century.

Deeper defense against cyber attacks

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To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Finding true protein hotspots in cancer research

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Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Getting to the bottom of deadly weather

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Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Competition sheds light on approximation methods for large spatial datasets

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Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

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A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Can machine learning help predict disease spread?

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Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Getting the tail straight

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KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

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A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Extreme analysis of seizures

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CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

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Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.

Better health from better data

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The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

A model for millions of locations

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CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Yuxiao Li

Ph.D., Statistics
Yuxiao Li obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. Education and Early Career: Yuxiao obtained his Bachelor of Science degree in Mathematics in 2013 from Beijing Institute of Technology, Beijing, China. In 2015, he graduated with his Master of Science degree in Statistics from the University of California at Irvine, USA. Then, he joined the Ph.D. program in Statistics at KAUST. Research Interest: His research interests lie in the fields such as

Xiran Zhang

Ph.D. Student, Statistics

Wanfang Chen

Ph.D., Statistics
Wanfang Chen obtained a Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. Education and Early Career Wanfang Chen obtained her Bachelor Degree of Science in Mathematics at Beijing Institute of Technology (BIT) in China in 2013. She obtained her Master Degree of Science in Econometrics at Xiamen University in China in 2016. She joined the Ph.D. program in Statistics at KAUST in 2016. Research Interest Spatio-temporal statistics, wind

Sihan Chen

Ph.D. Student, Statistics

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Sagnik Mondal

Ph.D. Student, Statistics
Sagnik Mondal is a PhD student in the Statistics program at KAUST. Prior to joining KAUST, Sagnik completed a Master of Science degree from the Indian Institute of Technology (IIT), Kanpur, India.

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration.

Mary Lai O. Salvana

Ph.D. Student, Statistics
Mary Salvaña is a PhD student in the Spatio-Temporal Statistics and Data Science group under the supervision of Prof. Marc Genton at the King Abdullah University of Science and Technology (KAUST). Education and Early Career MS Applied Mathematics graduate from the Ateneo de Manila University (Philippines) Research Interest Spatio-Temporal Statistics, Wind Energy Modelling, Renewable Energy Modelling.

Kesen Wang

Ph.D. Student, Statistics
Kesen Want is a MS student in the Statistics program at KAUST. Prior to joining KAUST, Kesen completed a Bachelor of Science degree from the University of Maryland College Park in the USA.

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Israel Martínez-Hernández

Postdoctoral Research Fellow, Statistics
Israel Martínez-Hernández is a postdoctoral fellow at the Spatio-Temporal Statistics & Data Science Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Israel Martínez-Hernández received his Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in December 2017. He joined KAUST as a Postdoctoral Research Fellow in February 2018. Research Interest Functional Data Analysis, Functional Time Series, Long Memory Process, NonLinear Cointegration, Spatial Functional Data Analysis, Time Series Analysis

Hanan Alahmadi

Ph.D. Student, Statistics

Haakon Bakka

Postdoctoral Research Fellow, Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Ercong Zhang

M.S. Student, Statistics
Ercong Zhang is a MS/Ph.D. student in the Statistics program at KAUST. Prior to joining KAUST, Ercong obtained his Bachelor of Science degree in Statistics from Southern University of Science and Technology (SUSTech), Shenzhen, China.

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Alibi Jangeldin

M.S. Student, Statistics
Alibi Jangeldin was an MSc in Statistical Data Science candidate in Spatio-Temporal Statistics Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Marc Genton. Education and Early Career Alibi obtained his bachelor degree in Applied Mathematics from Nazarbayev University in Kazakhstan in 2018. He also worked as a data scientist at Ernst & Young and General Prosecutor's Office of Kazakhstan. M.S. In Statistics from KAUST, Dec. 2019. Research Interest His research interest include data science, statistics and machine learning. Awards
<> machine learning (59)

Andrea Vanessa Rocha

Research Specialist, Statistics

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B9 L2 R2325

This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

David Gomez-Cabrero

Associate Professor, Bioscience
Professor Cabrero's research focuses on developing statistics and machine learning tools for understanding biological systems that address health-related applications, advance biomedical research and enhance our understanding of the molecular mechanisms of diseases with the potential to uncover novel therapeutic approaches.

On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning

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B9 L2 R2325

This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Computing solutions for biological problems

1 min read ·
Xin Gao’s research is building bridges between computer science and biology.

Tweaking tools to track tweets over time

1 min read ·
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

Flattening the sentimental curve

1 min read ·
-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Tracking the Curve: Analyzing the Emotional Response to COVID-19

1 min read ·
Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

KAUST Prospective Student: Damilya Saduakhas

1 min read ·
Damilya Saduakhas, 21 years old, from Kazakhstan, obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Damilya has been selected as a young scientist to participate in the summer Data Lab from Yessenov Foundation and will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group under the supervision of Professor David Bolin.

Meet KAUST prospective student: Pratik Nag

1 min read ·
Pratik Nag will join KAUST this fall as a Ph.D. candidate and member of the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Pratik holds a master's degree in statistical quality control and operation research, and a bachelor's degree in statistics, from the Indian Statistical Institute and the University of Calcutta, India.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

KAUST Environmental Statistics group publishes book exploring multivariate statistical analysis

1 min read ·
The KAUST Environmental Statistic (ES) research group has recently published a book titled "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches." The book is the outcome of the Core Research Grant (GRG) project led by KAUST Professor Ying Sun, Associate Professor of Statistics.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

KAUST paper wins 2020 Computational Optimization and Applications Best Paper Award

1 min read ·
KAUST Professor of Computer Science Peter Richtárik and his former student Nicolas Loizou, currently a postdoctoral researcher at Mila - Quebec Artificial Intelligence Institute and soon to take up an assistant professorship position at Johns Hopkins University, recently received the 2020 Computational Optimization and Applications (COAP) Best Paper Award.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Zainab A. Homoud

M.S. Student, Bioengineering
Research Interest Studying : Brain time series, fMRI, EEG, Signal Processing and Machine Learning for better Mental Disease Diagnosis and Consciousness Study. Previous Education Undergraduate study (B.S) in Biomedical Engineering and Health Science at the Joint program of North Carolina State University and UNC Chapel Hill.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data
Sameh Abdulah

Sameh Abdulah

Sameh Abdulah > Appointments, Roles and Affiliations, Hierarchical Computations on Manycore Architectures
Dr. Sameh Abdulah specializes in HPC, scalable algorithms, and climate modeling, with innovations receiving the ACM Gordon Bell Prize for Climate Modelling.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia. He joined KAUST as a

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

Djidenou Montcho

Ph.D. Student, Statistics
Djidenou (Hans) Montcho is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Hans obtained his Bachelor Degree in Applied Mathematics at the Federal University of Rio Grande in Brazil. He graduated with a Master of Science from the Federal University of Sao Carlos and the University of Sao Paulo. He has worked as a data scientist for a large credit bureau in Brazil. Research Interest His research interest is mainly in Bayesian and

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Amine Bejaoui

Amine Bejaoui was an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amine Bejaoui obtained his engineering degree with majors in Economics and Scientific management from Ecole Polytechnique de Tunisie (EPT) , Tunisia, in 2015. Research Interest The scientific interests of Amine Bejaoui lies in the area of statistics, machine learning and the applications of Random Matrix Theory. Education Profile M.Sc. in Statistics, KAUST, Aug 2018 – Present
<> Environmental Statistics (45)

Professor Ying Sun elected as a Fellow of the American Statistical Association

3 min read ·
Ying Sun joins the elite ranks of ASA Fellows in 2025 for her contributions to spatial statistics, mentoring and global outreach.

Workshop on Computational Space-Time Statistics

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B1 L4 R4102

Workshop on Computational Space-Time Statistics

Uncertainty Quantification of Tsunami Models

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B1

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

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B1 L4 R4102

LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

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B1 L4 R4102

In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Professor Sun receives the Environment Early Investigator Award 2017

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

1 min read ·
Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Second year Ph.D. student to receive top statistics award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Taking aim at outliers

1 min read ·
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Statistics plot pollution to inform policy

1 min read ·
The spatial variation in different air pollution components helps identify possible targets for pollution control.

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Expanding the scale of dangerous weather prediction

1 min read ·
A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.

KAUST Ph.D. student receives environmetrics best poster award

1 min read ·
Ghulam Qadir, a third-year Ph.D. student in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received a best poster award from the Italian Environmetrics Society (GRASPA) and the International Environmetrics Society (TIES) at the GRASPA 2019 conference held from July 15 to 16 in Pescara, Italy.

Shaking up the landslide model

1 min read ·
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Meet KAUST prospective student: Cristian Felipe Jiménez Varón

1 min read ·
Cristian Felipe Jiménez Varón is an applied mathematics graduate who will join KAUST from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering fromUNAL .

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

Modeling rainfall drop by drop

1 min read ·
A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Beating the curse of dimensionality

1 min read ·
By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

1 min read ·
Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Getting to the bottom of deadly weather

1 min read ·
Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Professor Marc Genton receives the prestigious 2024 Don Owen Award

1 min read ·
KAUST Professor Marc Genton, a pioneer in environmental statistics, was awarded the prestigious 2024 Don Owen Award for his outstanding contributions to research, consultation, and service to the statistical community.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

Yuxiao Li

Ph.D., Statistics
Yuxiao Li obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. Education and Early Career: Yuxiao obtained his Bachelor of Science degree in Mathematics in 2013 from Beijing Institute of Technology, Beijing, China. In 2015, he graduated with his Master of Science degree in Statistics from the University of California at Irvine, USA. Then, he joined the Ph.D. program in Statistics at KAUST. Research Interest: His research interests lie in the fields such as

Wu Wang

Postdoctoral Research Fellow, Statistics
Wu Wang is a postdoctoral fellow at King Abdullah University of Science and Technology (KAUST) working with Professor Ying Sun in the environmental statistics research group. Education Wu Wang received his Ph.D. degree in June 2017 from Fudan University, China. He joined KAUST in September 2017 Wu Wang received his Bachelor's degree in 2008 from East China Normal University Research Interest Wu Wang' s research interests lie in spatial varying coefficients regression with application in particulate matter modeling. He is also interested in functional data analysis, especially functional linear

Stefano Castruccio

Postdoctoral Research Fellow, Statistics
Stephano Castruccio is currently working as an Assistant Professor at the University of Notre Dame (USA). Stefano worked as a Post Doc in KAUST, under Distinguished Professor Marc Genton . He did his PhD from University of Chicago, USA Research Interests Spatio-temporal modeling, climate model sensitivity, hierarchical modeling, monitoring air pollution

Sabrina Vettori

Ph.D. Student, Statistics
Sabrina Vettori was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the joint supervision of Prof. Marc Genton and Prof. Raphaël Huser. She successfully defended her Ph.D. thesis entitled " Models and Inference for Multivariate Spatial Extremes" and graduated in December 2017; see his PhD thesis here. Her PhD committee was composed of Professors Marc Genton (chair), Raphaël Huser (co-chair), Anthony Davison (external examiner from EPFL, Switzerland), Ying Sun and Slim Alouini. After her PhD, Sabrina co-founded the successful KAUST start-up

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Luigi Lombardo

Postdoctoral Research Fellow, Statistics
Dr. Luigi Lombardo joined the Extreme Statistics Research Group and worked as Postdoctoral Research Fellow from April, 2016, to December, 2018, under the joined supervision of Prof. Raphaël Huser and Prof. Martin Mai. After his postdoc at KAUST, Luigi moved to ITC, University of Twente, Netherlands, where he embraced an academic career by becoming an Assistant Professor of Soil Science. See his personal website here. Education and early career Luigi Lombardo received his Ph.D. from Universita’ degli Studi di Palermo, Palermo, Italy in 2015. He joined KAUST as a postdoc in April 2016, and moved

Junho Lee

Postdoctoral Research Fellow, Statistics
Short Bio about yourself Junho Lee received both undergraduate and Master degrees in Statistics from Yonsei University, Seoul, Korea. After his Master degree and before beginning his Ph.D. study, Junho Lee had worked at Korea Credit Bureau as a researcher to develop the Credit Scoring system for Bank, Credit Card and Insurance industries in Korea. Before joining KAUST in August 2017, Junho Lee received his Ph.D. in Statistics from University of Wisconsin-Madison, USA. Here, at KAUST, Junho Lee is working at Environmental Statistics Group with Prof. Ying Sun. Education and Early Career Ph.D. in

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Huang Huang

Ph.D., Statistics
Career 2022-, Quantitative Researcher & Developer, Abu Dhabi Investment Authority (ADIA), UAE. 2019-2021, Research Scientist, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research statistical modeling of multivariate spatio-temporal data with large size Apply machine learning models to environmental data with comparisons to spatio-temporal statistical models Lead reading groups on spatial machine learning Collaborate with computer scientists in developing high-performance packages for large geostatistical inference Mentor students on their internship on research

Ghulam A. Qadir

Ph.D. Student, Statistics
Ghulam Qadir is a Ph.D. student in the Environmental Statistics group under the supervision of Prof. Ying Sun at the King Abdullah University of Science and Technology (KAUST). He believes that KAUST creates a perfect research-friendly environment where students are provided with excellent resources which allow them to turn their research creativity into valuable discoveries as well as make them ready for their future endeavors. Education and Early Career M.Sc. Statistics, Hindu College, University of Delhi, 2016 B.Sc. (Hons.) Statistics, Hindu College, University of Delhi, 2014 Research

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics
Cristian Felipe Jiménez Varón is a Ph.D. candidate in the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Cristian obtained a master's degree in applied mathematics from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering from UNAL. Research Interests Cristian's research interests include the analysis of large volumes of data, statistical modeling and environmental statistics.

Carolina D. Euan Campos

Postdoctoral Research Fellow, Statistics
Dr. Carolina Euan is a postdoctoral fellow at the Environmental Statistics Research Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Carolina Euan received her Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in 2016. She joined KAUST as a Postdoctoral Research Fellow in October 2016. Research Interest High dimensional Time Series, Spatio-Temporal Models, Non-Stationary Processes, Complex Data Visualization, Computational Statistics. Awards and Distinctions Sylvia Esterby Presentation Award

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> spatio-temporal statistics (36)

Professor Ying Sun elected as a Fellow of the American Statistical Association

3 min read ·
Ying Sun joins the elite ranks of ASA Fellows in 2025 for her contributions to spatial statistics, mentoring and global outreach.
KAUST-CEMSE-Public-colloquium-Ying-Sun-Spatio-Temporal-Statistics-In-Geo-Environmental-Data-Science

Spatio-Temporal Statistics in Geo-Environmental Data Science

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B9, L2, R2322

In this talk, I will discuss the contributions and ongoing research of my Environmental Statistics Research Group in the area of spatio-temporal statistics, with a particular focus on geo-environmental data science. Our work is primarily centered around the development and application of sophisticated statistical models that improve the understanding and management of environmental data characterized by their spatial and temporal variability. My group has made significant advances in developing better spatio-temporal models that effectively capture the complexities inherent in environmental datasets, as well as developing innovative software tools such as ExaGeoStat, ParallelVecchiaGP, and DeepKriging, which support the analysis of large-scale geostatistical datasets. During this presentation, I will also showcase our research contributions motivated by environmental applications, including multivariate time series visualization and clustering, panel data analysis for functional and spatial data, and statistical process monitoring.

Functional data depth and its application in the visualization of spatio-temporal covariance structures

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KAUST

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read ·
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

New paper accepted in JABES

1 min read ·
New accepted paper: Castro-Camilo, D., Huser, R., and Rue, H. (2019+), A spliced Gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting, Journal of Agricultural, Biological and Environmental Statistics, to appear [ PDF preprint]

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Using Statistical Models to Solve Environmental Problems

1 min read ·
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.

Meet KAUST Prospective Student: Amin Wu

1 min read ·
Amin Wu is a 24-year-old graduate who will join KAUST from the Communication University of China. Wu will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Environmental Statistics Research Group under the supervision of Professor Ying Sun. Wu wants to be a teacher or researcher and use her expertise and knowledge to make the world a better place.

Meet KAUST prospective student: Xiaotian Jin

1 min read ·
Xiaotian Jin is a graduate of Wenzhou-Kean University in China (a campus of Kean University in the USA). Xiaotian will join KAUST this fall as a M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics research group under the supervision of Professor David Bolin.

Meet KAUST new student: Qilong Pan

1 min read ·
Qilong Pan is a statistics graduate who joined KAUST in June 2021 from the Wuhan University of Technology, China. Pan is a M.S./Ph.D. student and member of the Environmental Statistics research group under the supervision of Professor Ying Sun.

Zipei Geng

Ph.D. Student, Statistics

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Ying Sun

Professor, Statistics
Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Xiaotian Jin

Ph.D. Student, Statistics
Xiaotian Jin is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics research group under the supervision of Professor David Bolin. Before joining KAUST, Xiaotian obtained a bachelor's degree in computer science from Wenzhou-Kean University, China. Research Interests Xiaotian's research interests include statistical modeling and Spatio-temporal statistics.

Wanfang Chen

Ph.D., Statistics
Wanfang Chen obtained a Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. Education and Early Career Wanfang Chen obtained her Bachelor Degree of Science in Mathematics at Beijing Institute of Technology (BIT) in China in 2013. She obtained her Master Degree of Science in Econometrics at Xiamen University in China in 2016. She joined the Ph.D. program in Statistics at KAUST in 2016. Research Interest Spatio-temporal statistics, wind

Soumya Das

Ph.D. Student, Statistics
Soumya Das is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton. Education and Early Career M.sc. (2015-17) from Indian Institute of Technology Kanpur, Uttar Pradesh, India B.Sc. (2012-15) from Ramakrishna Mission Residential College (Autonomous), Narendrapur, Kolkata, West Bengal, India Research Interests Cyclostationary processes, Non-parametric analysis, Non-stationary models, Skewed distributions, Spatio-temporal statistics, Threshold models. Awards and Distinctions The General Proficiency Medal for

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Qilong Pan

Ph.D. Student, Statistics
Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration.

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia. He joined KAUST as a

Mary Lai O. Salvana

Ph.D. Student, Statistics
Mary Salvaña is a PhD student in the Spatio-Temporal Statistics and Data Science group under the supervision of Prof. Marc Genton at the King Abdullah University of Science and Technology (KAUST). Education and Early Career MS Applied Mathematics graduate from the Ateneo de Manila University (Philippines) Research Interest Spatio-Temporal Statistics, Wind Energy Modelling, Renewable Energy Modelling.

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Israel Martínez-Hernández

Postdoctoral Research Fellow, Statistics
Israel Martínez-Hernández is a postdoctoral fellow at the Spatio-Temporal Statistics & Data Science Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Israel Martínez-Hernández received his Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in December 2017. He joined KAUST as a Postdoctoral Research Fellow in February 2018. Research Interest Functional Data Analysis, Functional Time Series, Long Memory Process, NonLinear Cointegration, Spatial Functional Data Analysis, Time Series Analysis

Fouzi Harrou

Research Scientist, Statistics
Dr. Harrou received an M.Sc. degree in telecommunications and networking from the University of Paris VI and the Ph.D. degree in systems optimization and security from the University Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He is currently a Research Scientist with the Division of Computer, Electrical, and

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Daniela Cisneros

Ph.D. Student, Statistics
Daniela Cisneros was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Daniela successfully defended her PhD thesis entitled " Extreme-Value Models and Graphical Methods for Spatial Wildfire Risk Assessment" on September 11th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Jorge Mateu (external examiner from Universitat Jaume I, Castillón, Spain), Hernando Ombao, and Matteo Parsani. For her next career steps, Daniela will return to Mexico where

David Bolin

Professor, Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.

Amanda Lenzi

Postdoctoral Research Fellow, Statistics
Amanda Lenzi is currently working as a Postdoctoral fellow under Mihai Anitescu, in Argonne National Laboratory, Chicago USA Amanda Lenzi was a Post-Doctoral fellow in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc G. Genton in his research group. Education and Early Carreer Amanda Lenzi completed her Ph.D. at the Technical University of Denmark (DTU) in 2017. She also holds a B.S. and an M.S. degree in Statistics from the University of Campinas (UNICAMP), Brazil. She joined KAUST in September 2017. Research Interest Her

Alibi Jangeldin

M.S. Student, Statistics
Alibi Jangeldin was an MSc in Statistical Data Science candidate in Spatio-Temporal Statistics Group in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Marc Genton. Education and Early Career Alibi obtained his bachelor degree in Applied Mathematics from Nazarbayev University in Kazakhstan in 2018. He also worked as a data scientist at Ernst & Young and General Prosecutor's Office of Kazakhstan. M.S. In Statistics from KAUST, Dec. 2019. Research Interest His research interest include data science, statistics and machine learning. Awards
<> artificial intelligence (29)
Najmeddine Dhieb

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Computer vision: Teaching computers how to see the world

1 min read ·
Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

KAUST Associate Professor Xiangliang Zhang talks about artificial intelligence

1 min read ·
Xiangliang Zhang, associate professor of computer science in the University's Computer, Electrical and Mathematical Science and Engineering division, recently gave an invited Early Career Spotlight Talk at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18) 2018. During the event—which was held in Stockholm, Sweden, from July 13 to 19—Zhang and 21 international peers presented on all areas of artificial intelligence (AI).

Tweaking tools to track tweets over time

1 min read ·
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

ActivityNet is featured in the 2019 Artificial Intelligence Index

1 min read ·
ActivityNet is featured in the AI Index as the benchmark for the algorithms that can recognize human actions and activities from videos.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Flattening the sentimental curve

1 min read ·
-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Tracking the Curve: Analyzing the Emotional Response to COVID-19

1 min read ·
Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

AI that's built to save lives

1 min read ·
Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Meet KAUST prospective student: Pratik Nag

1 min read ·
Pratik Nag will join KAUST this fall as a Ph.D. candidate and member of the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Pratik holds a master's degree in statistical quality control and operation research, and a bachelor's degree in statistics, from the Indian Statistical Institute and the University of Calcutta, India.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

KAUST research group win inaugural MCIT Digital Innovation Award

1 min read ·
A team of KAUST researchers have won first place in the Digital Research track at the inaugural Digital Innovation Awards held on Sunday, April 25, 2021. Launched by the Ministry of Communications and Information Technology (MCIT), the awards aim to “enhance the culture of innovation and entrepreneurship in digital research and development” in Saudi Arabia.

KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon

1 min read ·
A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants.

Ángel López Oriona

Postdoctoral Research Fellow, Statistics
Dr. López Oriona received his bachelor’s degree in Mathematics and his master’s degree in Statistics both from the University of Santiago de Compostela (Spain), his master’s degree in Big Data Analytics from the European University of Madrid (Spain), and his PhD in Statistics from the University of A Coruña (Spain). He was a Visiting Researcher at the Sapienza University of Rome (Italy), the University of Sydney (Australia), the Helmut Schmidt University of Hamburg (Germany), and Lancaster University (United Kingdom). Since September 2023, he is a Postdoctoral Fellow in the Environmental

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.
<> big data (26)

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

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B1 L4 R4102

Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Exploring science's fourth paradigm

1 min read ·
The recent KAUST Research Conference: Computational and Statistical Interface to Big Data brought together leading computer science researchers and statistical experts to discuss the current state and future of data science. Held on campus from March 19 to 21, the conference covered such data science topics as succinct data representation and storage; big data visualization; parallel and distributed algorithms for inference and optimization; and analysis of large graphs and networks.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Getting the inside track on street design

1 min read ·
Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

1 min read ·
The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Beating the curse of dimensionality

1 min read ·
By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the

Trio of tuning tools for modeling large spatial datasets

1 min read ·
Statistical tools help fine-tune the parameters and approximations of models used to make sense of large spatial datasets.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Souhaib Ben Taieb

Postdoctoral Research Fellow, Statistics
Dr. Souhaib Ben Taieb is an Assistant Professor in Computer Science at University of Mons (UMONS) in Belgium. He is also an Adjunct Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. From 2016 to 2018, Souhaib was a Lecturer in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. In 2015, he was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.
<> Statistics of extremes (26)
Simon Nik

Simon Nik

Postdoctoral Research Fellow, Statistics
Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany, with a thesis entitled " Stochastic modeling and statistical analyses of non-negative data and times series". He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany. He joined KAUST as a postdoc in December 2024. Research interests Simon's current research interests focus primarily on extreme

Workshop on Computational Space-Time Statistics

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B1 L4 R4102

Workshop on Computational Space-Time Statistics

Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

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B5 L5 R5220

This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

Prof. Miguel de Carvalho and Rodrigo Rubio (PUC, Chile) visit extSTAT

1 min read ·
Prof. Miguel Carvalho holds a research and teaching positions at PUC Chile, EPFL, Banco de Portugal, and UNL. He is an applied mathematical statistician with a variety of research interests including, inter alia, statistical inferences for small-probability events, geometrical statistics, methods for data visualization and graphical learning, econometrics, and medical diagnostic assessment.

Prof. Benjamin Shaby (Penn State University, USA) visits extSTAT

1 min read ·
Prof. Ben Shaby is an Assistant Professor in the Statistics Department and the Institute for CyberScience at Penn State, where he started in 2013. Before that, he was a postdoc with Cari Kaufman at UC Berkeley. Previous stop was in Durham at the Statistics and Applied Mathematical Institute (SAMSI) and Duke University. He did his Ph.D. work in Statistics at Cornell University with David Ruppert and Marty Wells. Before starting at Cornell, he spent his undergrad years at Stanford (go Cardinal!). He then spent two great years working for Chris Field at the Carnegie Institution's Department of

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

New paper accepted in JABES

1 min read ·
New accepted paper: Castro-Camilo, D., Huser, R., and Rue, H. (2019+), A spliced Gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting, Journal of Agricultural, Biological and Environmental Statistics, to appear [ PDF preprint]

New paper accepted in JGR - Earth Surface

1 min read ·
New accepted paper: Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2019+), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, Journal of Geophysical Research-Earth Surface [ PDF preprint]

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

Zhongwei Zhang

Ph.D. Student, Statistics
Zhongwei Zhang was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Zhongwei successfully defended his PhD thesis entitled " Flexible Extremal Dependence Models for Multivariate and Spatial Extremes" on October 25th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Jonathan Tawn (external examiner from Lancaster University, UK), David Bolin, and Ajay Jasra. For his next career steps, Zhongwei has accepted a postdoctoral position at the University of

Yan Gong

Ph.D. Student, Statistics
Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at

Xuanjie Shao

Ph.D. Student, Statistics
Xuanjie Shao is a M.S./Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Education and Early Career Xuanjie Shao obtained his Bachelor from the Southern University of Science and Technology, Shenzhen, China. He joined the M.S./Ph.D. program in Statistics at KAUST in 2020, and successfully defended his MS thesis on April 5th, 2022; see his MS thesis here. Since then, he has been working on his PhD research about spatial extremes. Research interests Xuanjie Shao's research interests focus on extreme-value

Rustam Bekishev

M.S. Student, Statistics
Rustam Bekishev obtained his MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After his time at KAUST, he got a job as a Senior Business Analyst at the Applied Economics Research Center ( AERC), Kazakhstan. Areas of expertise and current scientific interests Rustam Bekishev's research interests focus on statistics of extremes.

Sabrina Vettori

Ph.D. Student, Statistics
Sabrina Vettori was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the joint supervision of Prof. Marc Genton and Prof. Raphaël Huser. She successfully defended her Ph.D. thesis entitled " Models and Inference for Multivariate Spatial Extremes" and graduated in December 2017; see his PhD thesis here. Her PhD committee was composed of Professors Marc Genton (chair), Raphaël Huser (co-chair), Anthony Davison (external examiner from EPFL, Switzerland), Ying Sun and Slim Alouini. After her PhD, Sabrina co-founded the successful KAUST start-up

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Noura Alotaibi

Ph.D. Student, Statistics
Noura Alotaibi is a Ph.D. candidate in the Extreme Statistics Research Group (XSTAT) under the supervision of Professor Raphaël Huser. Before joining KAUST, Noura obtained an MSc. degree in statistics from Ohio University, United States. Research interests Noura Alotaibi's research interests focus on Statistics of extremes and applications.

Matthew Sainsbury-Dale

Postdoctoral Research Fellow, Statistics
Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia, with a thesis titled "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", which received the Examiners Commendation for Outstanding Thesis. He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Matthew obtained his PhD in 2024 from the University of Wollongong (UOW), Australia. He joined KAUST as a

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Enas Alahmadi

M.S. Student, Statistics
Enas Alahmadi is a Master student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Education and Early Career Enas was selected for the KAUST Gifted Student Program (KGSP) to pursue her undergraduate studies abroad. She completed her Bachelor of Arts and Sciences in Mathematics and Bachelor of Arts and Sciences in Economics in 2018 at Boston University (MS), United States. She also completed a two month-internship at KAUST under the supervision of Prof. Raphaël Huser and Dr. Daniela Castro-Camilo.

Daniela Cisneros

Ph.D. Student, Statistics
Daniela Cisneros was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Daniela successfully defended her PhD thesis entitled " Extreme-Value Models and Graphical Methods for Spatial Wildfire Risk Assessment" on September 11th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Jorge Mateu (external examiner from Universitat Jaume I, Castillón, Spain), Hernando Ombao, and Matteo Parsani. For her next career steps, Daniela will return to Mexico where

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Bingqing Yi

Postdoctoral Research Fellow, Statistics
Bingqing Yi is a PhD graduate from University of Pau and the University of Melbourne and is working with the XSTAT group as a Postdoctoral Research Fellow supervised by Professor Raphaël Huser. Education and Early Career Bingqing Yi obtained his Bachelor of Statistics from Zhongnan University of Economics and Law in 2013, Wuhan, China, and his Master of Statistics from the Australian National University. Then, he obtained his joint PhD from the University of Pau and the University of Melbourne in 2023. Research interests Bingqing Yi's research interests focus primarily on copula theory

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> Computer science (25)

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Unlocking complex interactions

1 min read ·
Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Noisy cells produce bursts of protein

1 min read ·
A new mathematical model explains how random factors affect the production of proteins within the cells.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Sharing movement aids learning

1 min read ·
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Exploring protein form to find function

1 min read ·
Existing systems for predicting protein structure are outperformed by a newly developed method.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Xiangliang Zhang joins CBRC

1 min read ·
Xiangliang Zhang, KAUST associate professor of computer science and principal investigator of the Machine Intelligence & Knowledge Engineering Lab, joined the Computational Bioscience Research Center (CBRC) on July 1, 2018. In CBRC, Xiangliang will work on the problems of modeling biological networks using machine learning models.

Simple statistics can be good enough

1 min read ·
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

1 min read ·
The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.
<> spatial statistics (25)

Kriging Asymptotics

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B1 L4 R4102

Spatial analyses often focus on spatial smoothing using the geostatistical technique known as kriging. Theoretical results regarding large sample convergence rates of kriging predictors remain elusive. By casting kriging as a variational problem, we develop an equivalent kernel approximation technique that can also lead to computational feasibility for large data problems.

A Distributed Implementation of the Multi-resolution Approximation for Very Large Spatial Data

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B1 L4 R4102

With data of rapidly increasing sizes in the environmental and geosciences such as satellite observations and high-resolution climate model runs, the spatial statistics community has recently focused on methods that are applicable to very large data. One such state-of-the-art method is the multi-resolution approximation (MRA), which was specifically developed with high performance computer architecture in mind.

Workshop on Statistical Process Monitoring and Risk Assessment for Engineering and Spatial Environmental Applications

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B1 L4 R4102

LIST OF SPEAKERS KAUST Environmental Statistics Group, CEMSE Division - Ying Sun (ying.sun@kaust.edu.sa), PI - Fouzi Harrou (fouzi.harrou@kaust.edu.sa), Postdoc - Huang Huang (huang.huang@kaust.edu.sa), PhD Student - Tianbo Chen (tianbo.chen@kaust.edu.sa), PhD Student - Rui Meng (rui.meng@kaust.edu.sa), Master Student - Sulaiman Binkhamis (sulaiman.binkhamis@kaust.edu.sa), Master Student Hydrology and Land Observation Group, BESE Division - Gaohong Yin (gaohong.yin@kaust.edu.sa), Master Student Collaborators - NorEddine Ghaffour (noreddine.ghaffour@kaust.edu.sa), co-I, WDRC - Matthew McCabe

The strength of multidisciplinary research: the joint example of statistics and geoscience

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B1 L4 R4102

Different scientific branches have the potential to develop topics which would provide visibility and fame. However, comparable if not greater milestones can be achieved when researchers from totally different fields join their efforts. This seminar will summarize the scientific journey of a former member of KAUST, which spent three years here as a postdoc in statistics coming from a pure geological background, combining the best out of the two worlds. Examples of the latest researches will be provided in the context of space, time and space-time statistics, bridging it with the underlying geoscientific research questions.

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

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B5 L5 R5220

This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.

A talk with PhD student Sabrina Vettori: Catastrophic events are forecastable, after all!

1 min read ·
Environmental disasters occur daily and hit almost every country around the world, taking a heavy toll on lives and economies. According to the United Nations Office for Disaster Risk Reduction, every year there are around 350 reported disasters causing more than 20,000 deaths and affecting the life of almost 100 million people with an overall economic damage of 66.5 billion US$. But this can change.

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

1 min read ·
Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.

Students shine on the statistics stage

1 min read ·
KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li have been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

CEMSE New Faculty: David Bolin, Associate Professor of Mathematical Statistics

1 min read ·
David Bolin is an associate professor of mathematical statistics who joined the KAUST Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division earlier this month from the Department of Mathematical Sciences at the University of Gothenburg. Before joining KAUST, Bolin received both his Ph.D. degree in mathematical statistics and M.S.c in engineering mathematics from Lund University, Sweden, in 2012 and 2007, respectively. Upon completing his Ph.D., he spent one year at Umeå University, Sweden, working as a postdoctoral fellow before moving to the Chalmers University of Technology. In 2016, Bolin became an associate professor of mathematics at the University of Gothenburg, where a year later he received the title of Docent in mathematical statistics.

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Zhuldyzay Baki

M.S. Student, Statistics
Zhuldyzay Baki obtained her MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After her time at KAUST, she got a job as a Risk Insurance Associate at PwC, Kazakhstan. Areas of expertise and current scientific interests Zhuldyzay Baki's research interests focus on statistics of extremes with applications to environmental data.

Zhongwei Zhang

Ph.D. Student, Statistics
Zhongwei Zhang was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Zhongwei successfully defended his PhD thesis entitled " Flexible Extremal Dependence Models for Multivariate and Spatial Extremes" on October 25th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Jonathan Tawn (external examiner from Lancaster University, UK), David Bolin, and Ajay Jasra. For his next career steps, Zhongwei has accepted a postdoctoral position at the University of

Yan Song

Postdoctoral Research Fellow, Statistics

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Noura Alotaibi

Ph.D. Student, Statistics
Noura Alotaibi is a Ph.D. candidate in the Extreme Statistics Research Group (XSTAT) under the supervision of Professor Raphaël Huser. Before joining KAUST, Noura obtained an MSc. degree in statistics from Ohio University, United States. Research interests Noura Alotaibi's research interests focus on Statistics of extremes and applications.

Joydeep Chowdhury

Postdoctoral Research Fellow, Statistics
Joydeep Chowdhury is a postdoctoral fellow in the Spatio-Temporal Statistics & Data Science research group of Professor Marc G. Genton. His current research activities focus on problems in multivariate statistics, spatial statistics and functional data analysis.

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors

Alexandre de Bustamante Simas

Senior Research Scientist, Statistics
<> Deep learning (21)

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Juan Pablo Muñoz Díaz

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)
<> Bayesian computational statistics (20)

Truth, Knowledge, P-Values, Bayes, & Inductive Inference

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B1 L4 R4102

The talk will concern the role of statistical thinking in the search for Truth. This will bring us to a discussion of P-values, which has been, and still is, a central concept in Statistics and is a critical and highly controversial tool of scientific research. Recently it has elicited much, sometimes heated, debates and discussions. The American Statistical Association (ASA) was even compelled to release an official statement in early March 2016 regarding this issue, and a psychology journal gone to the extreme of banning the use of P-values in articles appearing in its journal!

Exploring Non-Linear Interactions in Multivariate Time Series

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KAUST

Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.

Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

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B3 L5 R5209

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

Bayesian Deep Learning

Maurizio Filippone, Associate Professor, EURECOM, France

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B9 L3 R3128

The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.

Approximate Bayesian inference based on dense matrices and new features using INLA

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B4 L5 R5220

The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.

2015 Workshop on Computational Space-Time Statistics

1 min read ·
Organizers : Prof. Marc G. Genton , Chair of Spatio-Temporal Statistics and Data Science Group Prof. Raphael Huser, Chair of Extreme Statistics Group​ Prof. Ying Sun​, Chair of Environmental Statistics Group Confirmed speakers: Prof. Sudipto Banerjee, University of California Los Angeles, USA Prof. Souhaib Ben Taieb, Monash University, Australia Prof. Stefano Castruccio, Newcastle University, UK Prof. Miguel de Carvalho, Pontificia Universidad Catolica de Chile,​ Chile Prof. Clément Dombry, University of Franche-Comté , France Prof. Xuming He, University of Michigan, USA Prof. Hsin-Cheng Huang

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

KAUST Ph.D. candidate wins ISBA-BayesComp poster award at the International Society for Bayesian Analysis 2022

1 min read ·
Esmail Abdul Fattah, a KAUST Ph.D. candidate in statistics, won a poster award in the "junior-BayesComp-ISBA" category at the 30th meeting of the International Society for Bayesian Analysis (ISBA 2022) held from June 26 to July 1 in Montreal, Canada.

Zhedong Liu

Ph.D. Student, Statistics
Ph.D. degree in Statistics at the King Abdullah University of Science and Technology (KAUST).

Małgorzata Forystek

M.S. Student, Statistics
Statistics Master's student interested in Data Science

Janet van Niekerk

Research Scientist, Statistics
Janet van Niekerk is a Research Scientist at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Mathematical Statistics), University of Pretoria, 2014 PhD (Mathematical Statistics), University of Pretoria, 2017 Research interests Efficient Bayesian methods for practical implementation, statistics for medical applications, complex survival analysis, INLA. Awards World Bank Fund awardee, International Statistical Institute, 2013 and 2015 Best oral presentation, Statistical

Hanan Alahmadi

Ph.D. Student, Statistics

Haakon Bakka

Postdoctoral Research Fellow, Statistics
Haakon Bakka is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Haakon received his PhD degree from the Norwegian University of Science and Technology in Trondheim, Norway. Research Interest My focus is on applied Bayesian statistics, often with spatial or space-time models, including priors, constructing random effects, fast inference, software development and data analysis.

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gulzhan Tumenbayeva

Ph.D. Student, Statistics
Gulzhan Tumenbayeva is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Gulzhan obtained her Bachelor of Science in Mathematics, Diploma with Honors in 2017 from Nazarbayev University, Astana, Kazakhstan. Then, she joined the MS/PhD program in Statistics at KAUST in 2017. In 2018, she graduated her Master of Science in Statistics at KAUST. Research Interest Her research interest is mainly in Bayesian and computational Statistics as well as

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Djidenou Montcho

Ph.D. Student, Statistics
Djidenou (Hans) Montcho is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Hans obtained his Bachelor Degree in Applied Mathematics at the Federal University of Rio Grande in Brazil. He graduated with a Master of Science from the Federal University of Sao Carlos and the University of Sao Paulo. He has worked as a data scientist for a large credit bureau in Brazil. Research Interest His research interest is mainly in Bayesian and

Denis Rustand

Postdoctoral Research Fellow, Statistics
Denis Rustand is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Statistics), University of Southern Brittany, 2017 PhD (Biostatistics), University of Bordeaux, 2020 Research interests Bayesian computational statistics, survival analysis, applications of statistics to medical research, INLA. Awards Doctor Norbert Marx Award, 2021 Summer school grant, Univerity of Toronto, 2018 Académie Française, Jean Walter Zellidja grant, 2018 EHESP

Cristian Chiuchiolo

Cristian Chiuchiolo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Christian obtained his Bachelor Degree in Statistics in 2015 at Florence, Italy. H e graduated with a Master of Science in Statistics at Bologna, Italy in 2017. Research Interest His research interest is mainly in Bayesian and computational Statistics as well as Probability and applications in R-INLA. Honors and Awards Cristian was announced a SAS Certified Base

Abylay Zhumekenov

Ph.D. Student, Statistics
Abylay Zhumekenov is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group. Education and Early Career Abylay obtained his Bachelor of Science in Mathematics in 2017 from Nazarbayev University, Astana, Kazakhstan. He then obtained a Master of Science degree in Applied Mathematics from Nazarbayev University in 2019. Research Interests Abylay's main research interests are Bayesian computational statistics and numerical methods for large sparse systems.
<> applied mathematics (18)

Juan Pablo Muñoz Díaz

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

A clearer view of rainfall patterns

1 min read ·
Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

1 min read ·
By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

1 min read ·
A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

Getting the jump on industrial failures

1 min read ·
A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Keeping antennas at peak performance

1 min read ·
Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

A safe sweep of weather data

1 min read ·
A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Signaling success for 5th gen communications

1 min read ·
An efficient wireless signal optimization scheme achieves peak performance for next-generation mobile communications.

Getting a visual on complex data

1 min read ·
A new method for visually presenting complex data distributions provides a much-needed tool for management, analysis and interpretation.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Unleashing the potential of tethered drones

1 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.
<> computational statistics (15)

Andrea Vanessa Rocha

Research Specialist, Statistics

Professor Ying Sun elected as a Fellow of the American Statistical Association

3 min read ·
Ying Sun joins the elite ranks of ASA Fellows in 2025 for her contributions to spatial statistics, mentoring and global outreach.

Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking

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B1 L4 R4102

In this thesis defense, I will talk about two topics—computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data.

Coupled Sampling Methods for Filtering

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KAUST

This thesis focuses on the use of multilevel Monte Carlo methods to achieve optimal error versus cost performance for statistical computations in hidden Markov models as well as for unbiased estimation under four cases: nonlinear filtering, unbiased filtering, unbiased estimation of hessian, continuous linear Gaussian filtering.

Ángel López Oriona

Postdoctoral Research Fellow, Statistics
Dr. López Oriona received his bachelor’s degree in Mathematics and his master’s degree in Statistics both from the University of Santiago de Compostela (Spain), his master’s degree in Big Data Analytics from the European University of Madrid (Spain), and his PhD in Statistics from the University of A Coruña (Spain). He was a Visiting Researcher at the Sapienza University of Rome (Italy), the University of Sydney (Australia), the Helmut Schmidt University of Hamburg (Germany), and Lancaster University (United Kingdom). Since September 2023, he is a Postdoctoral Fellow in the Environmental

Ying Sun

Professor, Statistics
Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

David Bolin

Professor, Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Carolina D. Euan Campos

Postdoctoral Research Fellow, Statistics
Dr. Carolina Euan is a postdoctoral fellow at the Environmental Statistics Research Group at King Abdullah University of Science and Technology (KAUST). Education and Early Career Carolina Euan received her Ph.D. degree in Probability and Statistics from the Center for Research in Mathematics (CIMAT), Mexico, in 2016. She joined KAUST as a Postdoctoral Research Fellow in October 2016. Research Interest High dimensional Time Series, Spatio-Temporal Models, Non-Stationary Processes, Complex Data Visualization, Computational Statistics. Awards and Distinctions Sylvia Esterby Presentation Award

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> modeling (14)

Houcem Ben Salem

Intern (former), Information Science Lab
Research Intern at KAUST(King Abdullah University of Science and Technology) .

2018 Workshop on Statistics and Data Science

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Auditorium 0215 between Buildings 4 & 5

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

On mean-field game price models

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B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

The Indian summer monsoon keeps the Arabian Peninsula hot and dry

1 min read ·
Modeling shows that the Indian summer monsoon can trigger heatwaves and sandstorms on the Arabian Peninsula.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.
<> Time Series (14)

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

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B1 L4 R4102

Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.

Yongxin Zhu

Ph.D., Statistics
Yongxin Zhu was a MS student in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in his research group. Education and Early Career "I'm currently working as a data scientist. The knowledge I acquired at KAUST helps me to be sensitive about the potential usage of data and to analyze data in a rigorous and systematic way." - Yongxin Zhu Yongxin obtained his Bachelor of Science degree in Business and Finance (2017) from the New York University Shanghai, China. In 2018, he graduated with his Master of Science

Wagner Barreto-Souza

Research Scientist, Statistics
Wagner Barreto-Souza is a Research Scientist of the KAUST Biostatistics Group headed by Prof. Hernando Ombao. Early Career Professor of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. (2013-2020) Education Profile Ph.D. in Statistics. Universidade de São Paulo, SP, Brazil. (2012) M.S. in Statistics. Universidade Federal de Pernambuco, Recife, Brazil. (2008) B.S. in Statistics. Universidade Federal de Pernambuco, Recife, Brazil. (2006)

Siti Balqis Samdin

Postdoctoral Research Fellow, Statistics
Siti Balqis Samdin is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Siti Balqis obtained her Ph.D. and Bachelor of Engineering in Biomedical Engineering from Universiti Teknologi Malaysia (UTM) in 2017 and 2011, respectively. She worked as Research Assistant at UTM before joining KAUST in 2018. Research Interest Her research interest primarily lies in biomedical signals and image processing using statistical methods such as time-series

Sarbojit Roy

Postdoctoral Research Fellow, Statistics

Raanju Ragavendar Sundararajan

Postdoctoral Research Fellow, Statistics
Raanju Sundararajan is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Raaju received his Ph.D. degree in Statistics in 2018 from the Texas A&M University, Texas, United States. Research Interest His research interests lie in fields such as nonstationary time series, frequency domain, and nonparametric methods. Editorial Positions Referee for journals such as Statistics in Medicine and Brain Connectivity.

Paolo Victor Redondo

Ph.D., Statistics
Ph.D. Degree in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser and Prof. Hernando Ombao.

Oleg Gorshkov

Visiting Researcher, Statistics
Oleg Gorshkov is a Visiting Researcher working in collaboration with Professor Ombao at the Biostatistics Group of the Statistic Program of KAUST. Research Interest Oleg's research interests lie in fields such as fractal analysis and chaos theory; focusing on the following main topics: analysis of bio-medical time series signals (R-R intervals, stabilograms, encephalograms, step width and stride length time series ); bio-medical images (thermograms). Editorial Position Reviewer, IEEE Signal Processing Letters Reviewer, IET Systems Biology

Meini Tang

M.S. Student, Statistics
Meini Tang is an MS student in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Meini obtained her Bachelor of Science in Applied Statistics (2018) from Sun Yat-sen University, Guangzhou, China. Then, she joined the MS/Ph.D. program in Statistics at KAUST in 2017. Research Interest Her research interests lie in fields such as t ime series analysis, brain-network analysis, Bayesian statistics, and machine learning.

Matheus B. Guerrero

Ph.D. Student, Statistics
Matheus was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), doing research under the joint supervision of Prof. Raphaël Huser and Prof. Hernando Ombao. Matheus successfully defended his PhD thesis entitled " Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data" on April 4th, 2023; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Prof. Hernando Ombao (co-chair), Miguel de Carvalho (external examiner from the University of

Maicon J. Karling

Postdoctoral Research Fellow, Statistics
Education B.Sc. in Mathematics (2014) - University of Coimbra, Portugal. B.Ed. in Mathematics (2015) - Unisinos, Brazil. M.Sc. (2017) and Ph.D. (2021) in Mathematics: Probability and Statistics - Federal University of Rio Grande do Sul, Brazil. Research Interests Goodness-of-fit tests, multivariate statistics, skewed distributions, and time series.

Hernando Ombao

Professor, Statistics
KAUST Professor of Statistics Hernando Ombao is an expert in time series models and their applications to designed medical experiments on complex biological processes.

Guillermo C. Granados Garcia

Ph.D. Student, Statistics
Guillermo is a Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in his research group. Education and Early Career Guillermo obtained his Bachelor of Science in Actuary by the Universidad Nacional Autónoma de México. Then, he joined the MS/Ph.D. program in Statistics at KAUST in 2017. In 2018, he graduated his Master of Science in Statistics at KAUST. Research Interest His research interests lie in fields such as stochastic financial models, actuarial sciences, biostatistics, time series

Anass B. El-Yaagoubi

Ph.D. (former), Statistics
Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.
<> climate science (13)

Letting the data speak for itself

1 min read ·
A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Challenging statistics of weather extremes

1 min read ·
More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

The Indian summer monsoon keeps the Arabian Peninsula hot and dry

1 min read ·
Modeling shows that the Indian summer monsoon can trigger heatwaves and sandstorms on the Arabian Peninsula.

Chasing spatial patterns

1 min read ·
A more accurate way of resolving spatial patterns in weather could lead to better predictions of climate change.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

Statistics plot pollution to inform policy

1 min read ·
The spatial variation in different air pollution components helps identify possible targets for pollution control.

A sharper view of flood risk

1 min read ·
Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

1 min read ·
High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

1 min read ·
Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.
<> data science (13)

2018 Workshop on Statistics and Data Science

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Auditorium 0215 between Buildings 4 & 5

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

Transparent and Robust Causal Inference in Data Science

Carlos Cinelli, Ph.D. candidate, Department of Statistics, UCLA

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KAUST

The past few decades have witnessed rapid and unprecedented theoretical progress in the science of causal inference, ranging from the “credibility revolution” with the popularization of quasi-experimental designs, to the development of a complete solution to non-parametric identification with causal graphical models. Most of these theoretical progress, however, relies on strong, exact assumptions, such as the absence of unobserved common causes, or the absence of certain direct effects. Unfortunately, more often than not these assumptions are very hard to defend in practice. This leads to two undesirable consequences for applied quantitative work in the data-intensive sciences: (i) important research questions may be neglected, simply because they do not exactly match the requirements of current methods; or, (ii) researchers may succumb to making the required “identification assumptions” simply to justify the use of available methods, but not because these assumptions are truly believed (or understood). In this talk, I will discuss new theories, methods, and software for permitting causal inferences under more flexible and realistic settings. These tools empower scientists, and policymakers to both examine the sensitivity of causal inferences to violations of its underlying assumptions, and also to draw robust and trustworthy conclusions from settings in which traditional methods fail.

The KAUST 2022 Workshop on Statistics

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B2 B3 A0215

The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

The KAUST 2023 Workshop on Statistics

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B18 R4203

The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Lifting up female scientists

1 min read ·
Women in Data Science (WiDS) is like the Global Women's March for analytics; it's a phenomenon too big for just one city. WiDS is the largest data science conference on Earth—taking place in over 50 countries, it has attained more than 100,000 attendees and is tagged as a global movement. The annual conference aims to inspire and educate data scientists worldwide—regardless of gender—and support women in the field. This year's WiDS conference was held at Stanford University with more than 100 regional institutions all over the world participating, including KAUST.

Alumni Feature: Majed Alzahrani

1 min read ·
Working in one of the largest companies in the world is a dream come true for many. For Majed Alzahrani, a former Ph.D. student who worked under the supervision of Associate Professor Xin Gao, things couldn't be better. After graduating in 2017, he quickly joined Saudi Aramco as a data scientist in its Drilling Data Science unit.

2019 Statistics and Data Science Workshop

1 min read ·
From November 18 to 20 the KAUST CEMSE Division has hosted the 2019 Statistics and Data Science Workshop. The workshop, organized by Workshop Chair Professor Ying Sun from the KAUST Environmental Statistics Group, featured 17 keynote talks from eminent data science researchers from Asia, Europe, North America and on-campus. Talks included: personalized recommendation by learning from heterogeneous graph data; dynamic spatial panel models; networks, common shocks, and sequential exogeneity; unbiased Markov chain Monte Carlo, plus many more.

KAUST STAT students come together for second ASA chapter meeting

1 min read ·
Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Noura Alotaibi

Ph.D. Student, Statistics
Noura Alotaibi is a Ph.D. candidate in the Extreme Statistics Research Group (XSTAT) under the supervision of Professor Raphaël Huser. Before joining KAUST, Noura obtained an MSc. degree in statistics from Ohio University, United States. Research interests Noura Alotaibi's research interests focus on Statistics of extremes and applications.

Małgorzata Forystek

M.S. Student, Statistics
Statistics Master's student interested in Data Science

Jian Cao

Ph.D., Statistics
Jian Cao obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc Genton at the "spatio-temporal statistics and data science" research group. His research focused on Monte Carlo simulation, hierarchical matrices, tile-low-rank matrices, and solving high dimensional multivariate normal or Student-t probabilities. Education and Early Career Ph.D. degree from King Abdullah University of Science and Technology Master from Shanghai Jiaotong University Bachelor from University of Science and Technology of China

Eman Abdullah Kabbas

Deeply passionate about Bayesian statistics, Eman Kabbas is a Math/Statistics Lecturer in the General Studies Department at Jubail Industrial College (JIC). Currently, Eman is a Ph.D. candidate in Applied Mathematics and Computational Sciences at KAUST, specializing in Bayesian statistics under the supervision of Professor Håvard Rue.
<> earth science and engineering (13)

Securing future energy supply

1 min read ·
KAUST’s Earth Science and Engineering Program is underpinned by a drive to develop sustainable ways to get the best from natural resources and quantify environmental hazards.

Reconstructing the Red Sea's climate patterns

1 min read ·
An advanced numerical model is helping researchers better understand the variability of the Red Sea’s climate patterns.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Winds and waves winding down

1 min read ·
Wind intensities and wave heights in the Red Sea are decreasing over time—potential for a significant impact on its ecosystem.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

Diagnosing the impacts of health policy

1 min read ·
Study design allows more rigorous evaluation of complex healthcare interventions.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Simulating Red Sea water exchanges

1 min read ·
Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.
<> electrical engineering (12)

A different kind of light

1 min read ·
The discovery of the incandescent light bulb has transformed human existence. New LED technologies promise to be the next step forward.

Signal noise annoys no more

1 min read ·
Identifying noise in communication signals helps to filter out glitches and improve transmission quality.

Better routing for wireless networks

1 min read ·
An improved wireless communication protocol enables more robust links between remote electronic devices.

Wireless devices tune-in to cloud power

1 min read ·
Next-generation mobile networks can use cloud computing algorithms to manage the increasingly high data demands of users.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Lighting the way to the underwater Internet of things

1 min read ·
An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

Tiny, fast, accurate technology on the radar

1 min read ·
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Drones deliver on internet connectivity

1 min read ·
Hovering airborne vehicles could connect smart sensors to the internet of things.

Internet of underground things to reveal buried vistas

1 min read ·
Pinpoint mesh of smart underground objects could give real-time 3D readout of fossil fuel reserves.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Unleashing the potential of tethered drones

1 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.

To Boldly Go Where No Sensor Has Gone Before: The Movement to Place IoT in Radical New Spaces

1 min read ·
The rapid growth of the world’s population demands more natural resources, food, and space. World renowned physicist Stephan Hawking has argued that soon we will require another world to live on because we are running out of space and natural resources. This ever-increasing demand for resources and space needs novel technologies to explore natural resources, produce more crops, and explore outer space. Internet of X-things (X-IoT) is an enabling technology to overcome all of the above issues. The framework of X-IoT consists of three major categories as shown in Figure 1. The first one is the
<> marine science (12)

Unlocking the mysteries of Red Sea eddies

1 min read ·
First-time reports of the statistical properties of ‘whirlpools’ show they are frequent and seasonal.

Coral reef fish: not such a family affair

1 min read ·
Some coral reef fish species don’t settle very close to home.

The Red Sea models the future

1 min read ·
Detailed analysis of nutrient distribution and circulation in the Red Sea could provide a model for the future of the world’s oceans.

An ocean observatory for the Red Sea

1 min read ·
Studies conducted at the Saudi Aramco-KAUST Marine Environmental Research Center provide new insights into the physical and biological aspects of the Red Sea.

Feeding in the moonlight

1 min read ·
Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

Whale shark populations are young and transient

1 min read ·
A population study of whale sharks in the Red Sea reveals unique group dynamics.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Mining Red Sea bacteria for industrial potential

1 min read ·
Genome sequencing of two Red Sea bacteria highlights their potential as industrial workhorses.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.
<> uncertainty quantification (11)
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

Uncertainty Quantification of Tsunami Models

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B1

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Cross-disciplinary collaboration results in groundbreaking earthquake research

1 min read ·
For KAUST research assistant Dr. Ling Zhang and Ph.D. student Hoby Razafindrakoto, a class in statistics the two took together on campus was a serendipitous event. "Hoby and Ling came to my spatial statistics class in the spring of 2013," explained Marc Genton, Professor of Statistics in the Computer, Electrical and Mathematical Science and Engineering Division. "They had to do a project for the class, and their project morphed into a collaboration that produced two papers and an online tool for uncertainty quantification in earthquake source modeling." The group effort included P. Martin Mai

Study finds Red Sea may be cooling rather than warming

1 min read ·
A recent study by a team including KAUST Earth scientists and oceanographers revealed that surface temperatures in the Red Sea may be cooling rather than rising. Analysis of long-term data sets shows that the current high warming rates of the Red Sea appear to be a combined effect of global warming and natural long-duration changes in sea surface temperature. "Our study has revealed a sequence of alternating positive and negative trends in Red Sea surface temperatures. Over the next decades, the trends indicate a cooling phase that may counter the effects of global warming," stated Ibrahim

Red Sea temperatures to cool in coming decades

1 min read ·
Exploring the links between natural climate cycles and the sea-surface temperature of the Red Sea reveals a cooling trend during the next few decades.

KAUST professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Pratik Nag

Ph.D. Student, Statistics
Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Ibrahim Hoteit

Professor Ibrahim Hoteit is a faculty member at KAUST, specializing in the simulation, analysis, and prediction of weather and climate patterns across the Arabian Peninsula. His pioneering research leverages data-driven modeling systems to advance the understanding of climatic dynamics in the Red Sea and Arabian Gulf regions. Professor Hoteit's work not only contributes to regional climate science but also supports sustainable environmental management and policy development in these critical areas.
<> bioscience (10)

Juan Pablo Muñoz Díaz

Ali O Balubaid

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

Peak performance for proteins

1 min read ·
An improved peak fitting procedure enables a better determination of protein structures.

Unlocking complex interactions

1 min read ·
Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

Noisy cells produce bursts of protein

1 min read ·
A new mathematical model explains how random factors affect the production of proteins within the cells.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

Computer models combat malaria

1 min read ·
A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Epilepsy study shows the shape of things to come

1 min read ·
Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.
<> extreme-value theory (10)
Simon Nik

Simon Nik

Postdoctoral Research Fellow, Statistics
Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany, with a thesis entitled " Stochastic modeling and statistical analyses of non-negative data and times series". He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany. He joined KAUST as a postdoc in December 2024. Research interests Simon's current research interests focus primarily on extreme

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

Dr. Sebastian Engelke (EPFL) visits extSTAT

1 min read ·
Sebastian is an Ambizione fellow at EPF Lausanne with Anthony Davison. Sebastian did his studies in Mathematics at University of Göttingen and UC Berkeley, and he finished his PhD as a Deutsche Telekom Foundation fellow in 2013 at the University of Göttingen with Martin Schlather. His research interests are in extreme value theory, spatial statistics, graphical models and data science.

Prof. Benjamin Shaby (Penn State University, USA) visits extSTAT

1 min read ·
Prof. Ben Shaby is an Assistant Professor in the Statistics Department and the Institute for CyberScience at Penn State, where he started in 2013. Before that, he was a postdoc with Cari Kaufman at UC Berkeley. Previous stop was in Durham at the Statistics and Applied Mathematical Institute (SAMSI) and Duke University. He did his Ph.D. work in Statistics at Cornell University with David Ruppert and Marty Wells. Before starting at Cornell, he spent his undergrad years at Stanford (go Cardinal!). He then spent two great years working for Chris Field at the Carnegie Institution's Department of

PhD student Sabrina Vettori selected for an ENVR Student Paper Award. Congrats!

1 min read ·
PhD student Sabrina Vettori has been selected to receive a Best Student Paper Award from the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA) for her manuscript entitled "Bayesian clustering and dimension reduction in air pollutant multivariate extremes".

Yan Gong

Ph.D. Student, Statistics
Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Peng Zhong

Ph.D. Student, Statistics
Peng Zhong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Peng successfully defended his PhD thesis entitled " Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes" on April 4th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Daniel Cooley (external examiner from Colorado State University, US), Marc Genton, and Ajay Jasra. For his next career steps, Peng has accepted a postdoctoral fellowship

Matheus B. Guerrero

Ph.D. Student, Statistics
Matheus was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), doing research under the joint supervision of Prof. Raphaël Huser and Prof. Hernando Ombao. Matheus successfully defended his PhD thesis entitled " Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data" on April 4th, 2023; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Prof. Hernando Ombao (co-chair), Miguel de Carvalho (external examiner from the University of
<> IoT (10)

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

5G IoT Networks: from Massive access to URLLC

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B1 L3 R3119

Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.

Physical Layer Security of Wireless-powered NOMA-based Cognitive Radio Networks: Fundamentals & Challenges

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B1 L2 R2202

Abstract In the last decade, with the emergence of the internet of things (IoT) as well as machine-to-machine (M2M) paradigms, future wireless networks are targeting new critical services such as remote healthcare, surveillance, transportation, etc. In this regard, with the massive expected number of connected IoT and M2M devices, several requirements have been imposed by the regulatory bodies onto the deployment of 5G network. These requirements include, but not limited to, higher connectivity, reduced latency, as well as increased spectral and energy efficiency. Interestingly, the

Drones deliver on internet connectivity

1 min read ·
Hovering airborne vehicles could connect smart sensors to the internet of things.

To Boldly Go Where No Sensor Has Gone Before: The Movement to Place IoT in Radical New Spaces

1 min read ·
The rapid growth of the world’s population demands more natural resources, food, and space. World renowned physicist Stephan Hawking has argued that soon we will require another world to live on because we are running out of space and natural resources. This ever-increasing demand for resources and space needs novel technologies to explore natural resources, produce more crops, and explore outer space. Internet of X-things (X-IoT) is an enabling technology to overcome all of the above issues. The framework of X-IoT consists of three major categories as shown in Figure 1. The first one is the

KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon

1 min read ·
A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants.
<> optimization (10)
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Estimating the expected information gain in Bayesian optimal experimental design

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B9 L2 H1

Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.

Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

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KAUST

Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.

Powering the bottom line

1 min read ·
A risk-based optimization scheme boosts confidence and profitability for future mixed-technology power plants.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Samuel Horváth

Ph.D. Student, Statistics
Samuel Horváth is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Samuel studied Financial Mathematics at Comenius University in Bratislava, Slovakia and obtained his bachelor in 2017. Then, he joined the MS/PhD program in Statistics and Optimization at KAUST in 2017. In 2018, he had an internship (Machine Learning Intern) at Exponea for 3 months. Research Interest His research interest include Optimization for Big Data

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.
<> communications (9)

Prof. Alouini selected as an IEEE Communications Society Distinguished Lecturer for 2016 and 2017

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Congratulations to Prof. Alouini for being selected to give a Distinguished Lecture for 2016 and 2017 in IEEE Communications Society

Marie Sklodowska-Curie Fellowships in Photonics @ CEMSE – call 4 - 2018

1 min read ·
A group of professors working in the area of photonics at KAUST, most of them with CEMSE Division, offer hosting of outgoing Marie Sklodowska-Curie Fellowships in their research groups within the EU H2020 COFUND Postdoctoral Fellowship Programme MULTIPLY.

Lighting the way to the underwater Internet of things

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An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

Tiny, fast, accurate technology on the radar

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A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Drones deliver on internet connectivity

1 min read ·
Hovering airborne vehicles could connect smart sensors to the internet of things.

Internet of underground things to reveal buried vistas

1 min read ·
Pinpoint mesh of smart underground objects could give real-time 3D readout of fossil fuel reserves.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Tareq Al-Naffouri

Professor Al-Naffouri's research focuses on designing and analyzing wireless communication systems, particularly spectrum sharing, cognitive radio, energy efficiency and security within the context of advanced technologies like 5G.

Mohamed-Slim Alouini

Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering
KAUST is a fertile place for any researcher who wants to develop rigorous computational and engineering methods to solve real-world problems. Its unique support of science allows the most advanced scientific revolution.
<> human health (9)

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Computer models combat malaria

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A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Finding true protein hotspots in cancer research

1 min read ·
Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Getting to the bottom of deadly weather

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Cold still causes far more deaths than heat in India. A study of the correlation between temperature and mortality in the Indian city of Pune has found that cold, rather than heat, is by far the bigger killer.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

New database to "SpUR" on cancer research

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Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Extreme analysis of seizures

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CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.
<> Signal processing (9)

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Alaa Alameer Ahmad

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Abdullatif Al-Rabah

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.
<> Statistical Modeling (9)

Andrea Vanessa Rocha

Research Specialist, Statistics

The two faces of correlation

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KAUST

Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.

Meet KAUST prospective student: Cristian Felipe Jiménez Varón

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Cristian Felipe Jiménez Varón is an applied mathematics graduate who will join KAUST from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering fromUNAL .

Meet KAUST prospective student: Xiaotian Jin

1 min read ·
Xiaotian Jin is a graduate of Wenzhou-Kean University in China (a campus of Kean University in the USA). Xiaotian will join KAUST this fall as a M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics research group under the supervision of Professor David Bolin.

Xiaotian Jin

Ph.D. Student, Statistics
Xiaotian Jin is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics research group under the supervision of Professor David Bolin. Before joining KAUST, Xiaotian obtained a bachelor's degree in computer science from Wenzhou-Kean University, China. Research Interests Xiaotian's research interests include statistical modeling and Spatio-temporal statistics.

Jordan Richards

Postdoctoral Research Fellow, Statistics
Dr. Jordan Richards was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from October 2021 to January 2024, developing machine learning approaches for spatial extremes with environmental applications. After his postdoc at KAUST, Jordan moved to the University of Edinburgh, UK, where he embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor) in Statistics. See his personal website here. Education and early career Jordan Richards received his Ph.D. from Lancaster University under the supervision of Jonathan A. Tawn, Jennifer L

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics
Cristian Felipe Jiménez Varón is a Ph.D. candidate in the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Cristian obtained a master's degree in applied mathematics from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering from UNAL. Research Interests Cristian's research interests include the analysis of large volumes of data, statistical modeling and environmental statistics.

Arnab Hazra

Postdoctoral Research Fellow, Statistics
Dr. Arnab Hazra was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from February 2019 until December 2021, doing research on spatial extremes with environmental applications and bayesian statistics. After his postdoc at KAUST, Arnab moved to the Indian Institute of Technology (IIT), Kanpur, India, where he embraced an academic career by becoming an Assistant Professor of Statistics. See his personal website here. Education and early career Arnab Hazra received his Ph.D. in Statistics from North Carolina State University, United States. His Ph.D. advisors
<> Red Sea (8)

Winds and waves winding down

1 min read ·
Wind intensities and wave heights in the Red Sea are decreasing over time—potential for a significant impact on its ecosystem.

More rain for the Red Sea if El Niño breezes in

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Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

Red Sea flushes faster from far-flung volcanoes

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Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Simulating Red Sea water exchanges

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Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.

Remote sensing of toxic algal blooms

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Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Ibrahim Hoteit

Professor Ibrahim Hoteit is a faculty member at KAUST, specializing in the simulation, analysis, and prediction of weather and climate patterns across the Arabian Peninsula. His pioneering research leverages data-driven modeling systems to advance the understanding of climatic dynamics in the Red Sea and Arabian Gulf regions. Professor Hoteit's work not only contributes to regional climate science but also supports sustainable environmental management and policy development in these critical areas.
<> Bayesian Statistics (7)

Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

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B1 L2

In this talk, we introduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear to adequately explore the posterior tree space of this model.

Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

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B3 L5 R5209

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

New paper accepted in JGR - Earth Surface

1 min read ·
New accepted paper: Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2019+), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, Journal of Geophysical Research-Earth Surface [ PDF preprint]

CEMSE New Faculty: Ajay Jasra, Professor, Applied Mathematics and Computational Sciences

1 min read ·
Ajay Jasra is a professor of applied mathematics and computational sciences who recently joined the KAUST CEMSE Division in July 2019.

Yan Gong

Ph.D. Student, Statistics
Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at

Maurizio Filippone

Associate Professor, Statistics
KAUST Associate Professor of Statistics Maurizio Filippone’s primary research focus is Bayesian statistics, namely, enabling sound decision-making through uncertainty quantification in model parameters and predictions. His main research interests are in models based on deep learning and Gaussian processes

Amanda Lenzi

Postdoctoral Research Fellow, Statistics
Amanda Lenzi is currently working as a Postdoctoral fellow under Mihai Anitescu, in Argonne National Laboratory, Chicago USA Amanda Lenzi was a Post-Doctoral fellow in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Marc G. Genton in his research group. Education and Early Carreer Amanda Lenzi completed her Ph.D. at the Technical University of Denmark (DTU) in 2017. She also holds a B.S. and an M.S. degree in Statistics from the University of Campinas (UNICAMP), Brazil. She joined KAUST in September 2017. Research Interest Her
<> computational science (7)

KAUST climate research named finalist for ADIA Lab Climate Data Sciences Award

3 min read ·
KAUST team develops breakthrough method for wind data generation, earning finalist spot for ADIA Lab’s climate science award.

Low Rank Everywhere (Almost)

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B9 L2 R2322

Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Finding the Face in the Crowd

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An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Sharing movement aids learning

1 min read ·
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.
<> Computer Vision (7)

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Abdelrahman Eldesokey

Postdoctoral Research Fellow
I am a Postdoctoral Fellow at KAUST, Saudi Arabia, specializing in Generative AI and conducting research on advanced topics in Generative and Large Language Models (LLMs). I bring over 11 years of combined academic and industrial experience in Computer Vision and Deep Learning across Sweden, Egypt, and Saudi Arabia.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.
<> computing (7)

The two faces of correlation

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KAUST

Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

One bad apple can spoil a robot swarm

1 min read ·
Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Zooming in on brain function

1 min read ·
Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.
<> data analysis (7)

Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

High-dimensional Q-learning for Dynamic Treatment Regimes

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B3 L5 R5220

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

1 min read ·
Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

2018 Workshop on Statistics and Data Science

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The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). Details on the workshop (speakers, abstracts and schedule) can be found here.

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.

Zhedong Liu

Ph.D. Student, Statistics
Ph.D. degree in Statistics at the King Abdullah University of Science and Technology (KAUST).

Anass B. El-Yaagoubi

Ph.D. (former), Statistics
Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.
<> neuroscience (7)

Simulation of Neuronal Signal Processing - 2020-03-05

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B9 L2 R2322

In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

Connecting the dots to unlock brain structure

1 min read ·
Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

Stimulating deeper insights into brain function

1 min read ·
Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

Making collective sense of brainwaves

1 min read ·
A new statistical tool for collectively analyzing large sets of brainwaves promises to accelerate neurofunctional research.

A brainwave from social networks

1 min read ·
The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Anass B. El-Yaagoubi

Ph.D. (former), Statistics
Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.
<> award (6)

Ying Sun wins Young Researcher Award

1 min read ·
KAUST Assistant Professor of Statistics Ying Sun won the 2016 Abdel El-Shaarawi Young Researcher (AEYR) Award from the International Environmetrics Society (TIES) in June. The society was founded by El-Shaarawi, an Egyptian-born and Canadian-trained statistician, and the award was established by the board of TIES in 2002 to honor young statisticians who have made excellent contributions to the development of statistical and/or quantitative methods for environmental science research. Sun received the award on July 18 at the 26th Annual Conference of the International Environmetrics Society in

Assistant Professor Ying Sun wins American Statistical Association award

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​​​​Ying Sun, KAUST assistant professor of applied ​mathematics and computational science​, won the 2017 Section on Statistics and the Environment (ENVR) Early Investigator Award from the American Statistical Association (ASA). Sun received her award at the 2017 Joint Statistical Meetings (JSM), held from July 29 to August 3 in Baltimore, Maryland, U.S. Sun's award recognizes her important contributions to the field of environmental statistics that help to address major problems like climate change and environmental pollution. At KAUST, Sun leads the Environmental Statistics Group in the

Professor Sun receives the Environment Early Investigator Award 2017

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Postdoc Carolina Euan received Sylvia Esterby Presentation Award from TIES

1 min read ·
Carolina Euán, a postdoctoral fellow in KAUST Associate Professor Ying Sun's Environmental Statistics research group, recently received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) at the 28th Annual TIES Conference 2018 held from July 16 to 21, 2018, in Guanajuato, Mexico. Euán won the award for her talk entitled "Bernoulli Vector Autoregressive Model with Applications to Spatio-temporal Drought Events in Mexico."

KAUST alumna wins ADIPEC Young Engineer of the Year Award

1 min read ·
KAUST alumna Shamael Al-Shuhail won the prestigious Abu Dhabi International Exhibition & Conference (ADIPEC) Young Engineer of the Year Award at ADIPEC 2018 held in Abu Dhabi, UAE.

Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

1 min read ·
Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.
<> bayesian inference (6)

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

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B5 L5 R5220

In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.

The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics

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B1 L4 R4102

In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.

KAUST professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

Rishikesh Yadav

Ph.D. Student, Statistics
Rishikesh Yadav was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Rishikesh successfully defended his PhD thesis entitled " Bayesian Modeling of Sub-Asymptotic Spatial Extremes" on April 5th, 2022; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Philippe Naveau (external examiner from CNRS, France), Marc Genton, and Ajay Jasra. For his next career steps, Rishikesh has accepted a postdoctoral position at HEC Montréal, Canada, under the joint supervision

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.
<> computational methods (6)

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read ·
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Using Statistical Models to Solve Environmental Problems

1 min read ·
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.
<> copulas (6)

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Yan Gong

Ph.D. Student, Statistics
Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Pavel Krupskiy

Postdoctoral Research Fellow, Statistics
Krupskiy, Pavel Postdoctoral Fellows ​​Ph.D. 2014, University of British Columbia, Canada​​ Now Assistant Professor at University of Melbourne Education ​ Ph.D. in Statistics 2014, University of British Columbia, Canada M.A. in Economics 2009, New Economic School, Russia B.Sc. in Mathematics 2006, Lomonosov Moscow State University, Russia Research interests copulas, multivariate extremes, nonparametric statistics, spatial data

Bingqing Yi

Postdoctoral Research Fellow, Statistics
Bingqing Yi is a PhD graduate from University of Pau and the University of Melbourne and is working with the XSTAT group as a Postdoctoral Research Fellow supervised by Professor Raphaël Huser. Education and Early Career Bingqing Yi obtained his Bachelor of Statistics from Zhongnan University of Economics and Law in 2013, Wuhan, China, and his Master of Statistics from the Australian National University. Then, he obtained his joint PhD from the University of Pau and the University of Melbourne in 2023. Research interests Bingqing Yi's research interests focus primarily on copula theory
<> functional data analysis (6)

Functional data depth and its application in the visualization of spatio-temporal covariance structures

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KAUST

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

Multivariate Functional Data Analysis and Visualization

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B3 L5 R5220

As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

CEMSE New Faculty: Joaquín Ortega Sánchez, Instructional Professor, Statistics

1 min read ·
Joaquín Ortega Sánchez is an instructional professor of statistics who joins the KAUST CEMSE Division after spending the last sixteen years working at the Mathematics Research Center (CIMAT) in Guanajuato, Mexico. Born in Venezuela, Ortega completed his postgraduate and graduate studies in London, where he studied mathematics at King’s College London, before obtaining his Ph.D. in Probability Theory across the city at Imperial College London. After his time in the U.K., he returned to his native Venezuela where he worked for over 20 years at the Universidad Central de Venezuela, Caracas. Over the course of his career, Ortega’s research work has focused on stochastic processes, specifically Gaussian processes and time series with applications in oceanography and biostatistics. And more recently his work has focused on functional data analysis.

Joaquin Ortega Sanchez

Instructional Professor, Statistics
Joaquín Ortega Sánchez is an instructional professor of statistics whose work has focused on stochastic processes, specifically Gaussian processes and time series, and more recently, functional data analysis.

Joydeep Chowdhury

Postdoctoral Research Fellow, Statistics
Joydeep Chowdhury is a postdoctoral fellow in the Spatio-Temporal Statistics & Data Science research group of Professor Marc G. Genton. His current research activities focus on problems in multivariate statistics, spatial statistics and functional data analysis.

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics
Cristian Felipe Jiménez Varón is a Ph.D. candidate in the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Cristian obtained a master's degree in applied mathematics from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering from UNAL. Research Interests Cristian's research interests include the analysis of large volumes of data, statistical modeling and environmental statistics.
<> stochastic differential equations (6)

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

Nikolas Kantas, Associate Professor, Department of Mathematics, Imperial College London

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KAUST

We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

David Bolin

Professor, Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.
<> 6G (5)
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

6G: The time is now!

1 min read ·
While 5G companies and opponents are competing over the deployment of the first 5G platforms, the next generation of wireless telecommunication technology is being concocted right now in laboratories all around the world.

New research aims to bridge the digital divide

1 min read ·
By the end of 2018, there were nearly 4 billion people without Internet connectivity in the world. Almost 75% of this unconnected population is located in 20 countries and is concentrated in rural, low-income, and low-literacy areas.

Connecting the Unconnected at the KAUST 6G Summit

1 min read ·
The inaugural 6G Summit on "Connecting the Unconnected" took place on Tuesday, October 27, and featured a host of international researchers and experts in the areas of Internet and Communication Technology (ICT) and Wireless Communications System (WCS).

Shaping a 6G future

1 min read ·
This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.
<> applied statistics (5)

Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

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KAUST

Our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters.

Solvers for Coupled Problems in Porous Media Science

Arne Naegel

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B9 L2 R2322

No abstract is available.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.

Amine Bejaoui

Amine Bejaoui was an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amine Bejaoui obtained his engineering degree with majors in Economics and Scientific management from Ecole Polytechnique de Tunisie (EPT) , Tunisia, in 2015. Research Interest The scientific interests of Amine Bejaoui lies in the area of statistics, machine learning and the applications of Random Matrix Theory. Education Profile M.Sc. in Statistics, KAUST, Aug 2018 – Present
<> Biostatistics (5)

Meet KAUST new student: Daniel Osorio Barreto

1 min read ·
Daniel Osorio Barreto, is an economics graduate who joined KAUST this fall from Universidad Autónoma de Manizales, Colombia. He is as a Ph.D. student and member of the KAUST Biostatistics Group under the supervision of Professor Hernando Ombao.

Shuhao Jiao

Postdoctoral Research Fellow, Statistics
Shuhao Jiao is a Postdoctoral Fellow of the KAUST Biostatistics Research Group. Before joining KAUST, Dr. Jiao received his Ph.D. in Statistics in 2019 from University of California, Davis. He is interested in statistical learning of complicated neuroimage data. He addresses these problems using novel statistical theory and methods in functional data analysis and machine learning. Education 2019: Ph.D., Department of Statistics, University of California, Davis. 2014: BSc (Mathematics & Statistics), Shandong University, Jinan, China. Research Interest Functional data analysis, Time series

Mara Sherlin Talento

Ph.D. Student, Statistics
We do not gain confidence through measuring what we know, but through quantifying how much we do not know. Mara Sherlin Talento is a PhD student in the Biostatistics group of King Abdullah University of Science and Technology, under the supervision of Prof. Hernando Ombao. She finished her Master’s degree in University of the Philippines and served the chairperson of Young Statisticians Committee of the International Statistical Institute during 2020 – 2023. She is also a member of American Statistical Association and International Biometric Society.

Janet van Niekerk

Research Scientist, Statistics
Janet van Niekerk is a Research Scientist at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career MSc (Mathematical Statistics), University of Pretoria, 2014 PhD (Mathematical Statistics), University of Pretoria, 2017 Research interests Efficient Bayesian methods for practical implementation, statistics for medical applications, complex survival analysis, INLA. Awards World Bank Fund awardee, International Statistical Institute, 2013 and 2015 Best oral presentation, Statistical

Daniel Osorio Barreto

Ph.D. Student, Statistics
Daniel Osorio Barreto is a Ph.D. student and member of the KAUST Biostatistics Group under the supervision of Professor Hernando Ombao. Before joining KAUST, Daniel obtained a bachelor's degree in Economics from Universidad Autónoma de Manizales and an M.Sc. in Economics from Universidad de Manizales, Colombia. Research Interests Daniel's research interests include biostatistics, behavioral economics, and macroeconomics.
<> environmental applications (5)

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read ·
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.

Ying Sun

Professor, Statistics
Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Damilya Saduakhas

Ph.D. Student, Statistics
Damilya Saduakhas is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group (StochProc) under the supervision of Professor David Bolin. Before joining KAUST, Damilya obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Research Interests Damilya's research interests include geospatial statistics, multivariate spatial processes, environmental modeling, and applications.
<> extreme statistics (5)

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Modeling the neighborhood boosts landslide prediction

1 min read ·
A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Paolo Victor Redondo

Ph.D., Statistics
Ph.D. Degree in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser and Prof. Hernando Ombao.
<> extreme weather (5)

How extreme weather exacerbates air pollution

1 min read ·
Simultaneously modeling of air pollutants and weather under extreme conditions highlights the potential for serious health risks.

Reining in computational complexity

1 min read ·
A more efficient approach to modeling spatial data involving thousands of variables keeps computation time in check.

New paper accepted in JABES

1 min read ·
New accepted paper: Castro-Camilo, D., Huser, R., and Rue, H. (2019+), A spliced Gamma-generalized Pareto model for short-term extreme wind speed probabilistic forecasting, Journal of Agricultural, Biological and Environmental Statistics, to appear [ PDF preprint]

Heatwave model shows it is hotter in more places, more often

1 min read ·
New modeling shows that heatwaves across Europe have increased in both frequency and spatial extent over the past century.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.
<> Gaussian processes (5)

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

CEMSE New Faculty: Joaquín Ortega Sánchez, Instructional Professor, Statistics

1 min read ·
Joaquín Ortega Sánchez is an instructional professor of statistics who joins the KAUST CEMSE Division after spending the last sixteen years working at the Mathematics Research Center (CIMAT) in Guanajuato, Mexico. Born in Venezuela, Ortega completed his postgraduate and graduate studies in London, where he studied mathematics at King’s College London, before obtaining his Ph.D. in Probability Theory across the city at Imperial College London. After his time in the U.K., he returned to his native Venezuela where he worked for over 20 years at the Universidad Central de Venezuela, Caracas. Over the course of his career, Ortega’s research work has focused on stochastic processes, specifically Gaussian processes and time series with applications in oceanography and biostatistics. And more recently his work has focused on functional data analysis.

Meet KAUST Prospective Student: Amin Wu

1 min read ·
Amin Wu is a 24-year-old graduate who will join KAUST from the Communication University of China. Wu will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Environmental Statistics Research Group under the supervision of Professor Ying Sun. Wu wants to be a teacher or researcher and use her expertise and knowledge to make the world a better place.

Maurizio Filippone

Associate Professor, Statistics
KAUST Associate Professor of Statistics Maurizio Filippone’s primary research focus is Bayesian statistics, namely, enabling sound decision-making through uncertainty quantification in model parameters and predictions. His main research interests are in models based on deep learning and Gaussian processes

Joaquin Ortega Sanchez

Instructional Professor, Statistics
Joaquín Ortega Sánchez is an instructional professor of statistics whose work has focused on stochastic processes, specifically Gaussian processes and time series, and more recently, functional data analysis.
<> geospatial statistics (5)

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Better health from better data

1 min read ·
The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

Hanan Alahmadi

Ph.D. Student, Statistics

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Damilya Saduakhas

Ph.D. Student, Statistics
Damilya Saduakhas is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group (StochProc) under the supervision of Professor David Bolin. Before joining KAUST, Damilya obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Research Interests Damilya's research interests include geospatial statistics, multivariate spatial processes, environmental modeling, and applications.
<> healthcare (5) <> Reinforcement Learning (5)

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering
Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Aznaur Aliev

M.S. Student

We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
<> Spatial extremes (5)

Statistics of Extremes

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B9 L2 H1 R2322

Extreme environmental events such as droughts, floods and heat-waves take place in space and time, and it is necessary to take this into account when evaluating their risks and estimating their probabilities. During this seminar, I will review some classical and more recent work on this topic, focusing on the modeling of univariate and spatial extremes. The ideas will be illustrated by applications to peak river flow data from the UK, and heavy rainfall close to Jeddah.

Dr. Sebastian Engelke (EPFL) visits extSTAT

1 min read ·
Sebastian is an Ambizione fellow at EPF Lausanne with Anthony Davison. Sebastian did his studies in Mathematics at University of Göttingen and UC Berkeley, and he finished his PhD as a Deutsche Telekom Foundation fellow in 2013 at the University of Göttingen with Martin Schlather. His research interests are in extreme value theory, spatial statistics, graphical models and data science.

Prof. Benjamin Shaby (Penn State University, USA) visits extSTAT

1 min read ·
Prof. Ben Shaby is an Assistant Professor in the Statistics Department and the Institute for CyberScience at Penn State, where he started in 2013. Before that, he was a postdoc with Cari Kaufman at UC Berkeley. Previous stop was in Durham at the Statistics and Applied Mathematical Institute (SAMSI) and Duke University. He did his Ph.D. work in Statistics at Cornell University with David Ruppert and Marty Wells. Before starting at Cornell, he spent his undergrad years at Stanford (go Cardinal!). He then spent two great years working for Chris Field at the Carnegie Institution's Department of

PhD student Sabrina Vettori selected for an ENVR Student Paper Award. Congrats!

1 min read ·
PhD student Sabrina Vettori has been selected to receive a Best Student Paper Award from the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA) for her manuscript entitled "Bayesian clustering and dimension reduction in air pollutant multivariate extremes".

Best 2016 Paper in the Journal of Agricultural, Biological and Environmental Statistics

1 min read ·
Professor Huser will present the work in a special invited session (JABES showcase) at the International Biometric Conference taking place in Barcelona, Spain, in July 2018.
<> Wireless Communications (5)

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Connecting the Unconnected at the KAUST 6G Summit

1 min read ·
The inaugural 6G Summit on "Connecting the Unconnected" took place on Tuesday, October 27, and featured a host of international researchers and experts in the areas of Internet and Communication Technology (ICT) and Wireless Communications System (WCS).

KAUST Distinguished Professor Mohamed-Slim Alouini honored with IEEE accolades

1 min read ·
Professor Mohamed-Slim Alouini has been awarded both the 2019 Institute of Electrical and Electronics Engineers (IEEE) Communication Society Communication Theory Technical Committee (CCTC) Technical Achievement Award, and the 2020 IEEE Vehicular Technology Society James Evans Avant Garde Award. The CEMSE Division founding faculty member received the awards for his contributions in the modeling, design, performance evaluation, and optimization of wireless communication systems and networks.

Shaping a 6G future

1 min read ·
This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.
<> AI (4) <> algorithm (4) <> brain science (4) <> climate change (4) <> cognitive radio systems (4)

Prof. Alouini selected as an IEEE Communications Society Distinguished Lecturer for 2016 and 2017

1 min read ·
Congratulations to Prof. Alouini for being selected to give a Distinguished Lecture for 2016 and 2017 in IEEE Communications Society

Marie Sklodowska-Curie Fellowships in Photonics @ CEMSE – call 4 - 2018

1 min read ·
A group of professors working in the area of photonics at KAUST, most of them with CEMSE Division, offer hosting of outgoing Marie Sklodowska-Curie Fellowships in their research groups within the EU H2020 COFUND Postdoctoral Fellowship Programme MULTIPLY.

A breath of fresh information to help diagnosis

1 min read ·
A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Mohamed-Slim Alouini

Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering
KAUST is a fertile place for any researcher who wants to develop rigorous computational and engineering methods to solve real-world problems. Its unique support of science allows the most advanced scientific revolution.
<> computational predictions (4)

Professor Marc Genton named Distinguished Professor of Statistics at KAUST

1 min read ·
Marc G.Genton, Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), has been appointed by KAUST President Jean-Lou Chameau to Distinguished Professor of Statistics.

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read ·
Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017. Genton and Castruccio's paper was entitled "Compressing an ensemble with

Marc Genton received an ISI Service Award 2019

1 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.
<> COVID-19 (4)

A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis

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KAUST

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this talk, I will introduce our recent work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) an embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

AI that's built to save lives

1 min read ·
Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).
<> data (4) <> environment (4) <> genomics (4)

David Gomez-Cabrero

Associate Professor, Bioscience
Professor Cabrero's research focuses on developing statistics and machine learning tools for understanding biological systems that address health-related applications, advance biomedical research and enhance our understanding of the molecular mechanisms of diseases with the potential to uncover novel therapeutic approaches.

Exploring protein form to find function

1 min read ·
Existing systems for predicting protein structure are outperformed by a newly developed method.

Mining Red Sea bacteria for industrial potential

1 min read ·
Genome sequencing of two Red Sea bacteria highlights their potential as industrial workhorses.

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.
<> MATLAB (4) <> nonparametric statistics (4)

Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

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B1 L2

In this talk, we introduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear to adequately explore the posterior tree space of this model.

Ziling Ma

Ph.D. Student, Statistics
Ziling is a doctoral student in Statistics at King Abdullah University of Science and Technology (KAUST), where she is conducting her research under the mentorship of Professor Hernando Ombao in the Biostatistics research group and Professor Ying Sun in the Environmental Statistics research group. Education and Initial Professional Experience Ziling Ma earned her Bachelor's degree in Mathematics and Applied Mathematics from Tianjin Normal University in China. She then furthered her education at KU Leuven in Belgium, where she was awarded a Master's degree in Mathematics. Ziling embarked on her

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Pavel Krupskiy

Postdoctoral Research Fellow, Statistics
Krupskiy, Pavel Postdoctoral Fellows ​​Ph.D. 2014, University of British Columbia, Canada​​ Now Assistant Professor at University of Melbourne Education ​ Ph.D. in Statistics 2014, University of British Columbia, Canada M.A. in Economics 2009, New Economic School, Russia B.Sc. in Mathematics 2006, Lomonosov Moscow State University, Russia Research interests copulas, multivariate extremes, nonparametric statistics, spatial data
<> numerical analysis (4)
Georgios Grekas

Georgios Grekas

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

Vaibhav Mehandiratta

Postdoctoral Research Fellow, Statistics
Vaibhav Mehandiratta is a Post-Doctoral fellow In the CEMSE Division, Statistics program, at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor David Bolin. Vaibhav received his PhD from the Department of Mathematics, Indian Institute of Technology Delhi in November 2022. He joined KAUST as a Post-Doctoral fellow in January 2023.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.
<> Partial Differential Equations (4)

Katerina Nik

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

Estimation Problems for Partial and Fractional Differential Equations - Spring 2019

-

B9 L2 H1

We will present some new methods for source and parameters estimation for partial and fractional differential equations and illustrate the results with some simulations and real applications.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

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B9 L2 H1 R2322

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

Alexandre de Bustamante Simas

Senior Research Scientist, Statistics
<> sensors (4) <> Stochastic processes (4) <> student award (4)

KAUST Ph.D. student wins American Statistical Association paper competition

1 min read ·
Sabrina Vettori, a Ph.D. student co-supervised by KAUST Distinguished Professor Marc Genton, who leads the Spatio-Temporal Statistics & Data Science group, and KAUST Assistant Professor Raphaël Huser, who leads the Extreme Statistics Research Group, won the 2017 Student Paper Competition sponsored by the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA). ​ Eligible papers focused on environmental statistics, and the four competition winners presented their papers at an ENVR-sponsored session at the 2017 Joint Statistical Meetings (JSM), held from

Ph.D. Student Gaurav Agarwal received an Honorable Mention from ENVR Student Paper Competition

1 min read ·
Ph.D. student Gaurav Agarwal received an Honorable Mention from ENVR student paper competition for his paper entitled "Quantile function modeling with application to salinity tolerance analysis in plant data."

Ph.D. Student Yuxiao Li won the ENAR Distinguished Student Paper Award

1 min read ·
KAUST Ph.D. student Yuxiao Li has been selected by the Eastern North American Region (ENAR) of the International Biometric Society to receive a Distinguished Student Paper Award at the 2018 ENAR Spring Meeting.

Ph.D. Student Yuxiao Li won the ENVR student paper award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."
<> visualization (4)

Professor Marc Genton named Distinguished Professor of Statistics at KAUST

1 min read ·
Marc G.Genton, Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), has been appointed by KAUST President Jean-Lou Chameau to Distinguished Professor of Statistics.

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read ·
Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017. Genton and Castruccio's paper was entitled "Compressing an ensemble with

Marc Genton received an ISI Service Award 2019

1 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.
<> weather (4) <> algorithms (3) <> Bayesian Estimation (3)

Study finds Red Sea may be cooling rather than warming

1 min read ·
A recent study by a team including KAUST Earth scientists and oceanographers revealed that surface temperatures in the Red Sea may be cooling rather than rising. Analysis of long-term data sets shows that the current high warming rates of the Red Sea appear to be a combined effect of global warming and natural long-duration changes in sea surface temperature. "Our study has revealed a sequence of alternating positive and negative trends in Red Sea surface temperatures. Over the next decades, the trends indicate a cooling phase that may counter the effects of global warming," stated Ibrahim

Red Sea temperatures to cool in coming decades

1 min read ·
Exploring the links between natural climate cycles and the sea-surface temperature of the Red Sea reveals a cooling trend during the next few decades.

Ibrahim Hoteit

Professor Ibrahim Hoteit is a faculty member at KAUST, specializing in the simulation, analysis, and prediction of weather and climate patterns across the Arabian Peninsula. His pioneering research leverages data-driven modeling systems to advance the understanding of climatic dynamics in the Red Sea and Arabian Gulf regions. Professor Hoteit's work not only contributes to regional climate science but also supports sustainable environmental management and policy development in these critical areas.
<> bioinformatics (3) <> climatology (3) <> complex models (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> Dynamical Systems (3) <> embedded systems (3) <> extreme computing (3) <> FD-MIMO (3) <> Functional data (3)

Classification of Functional Data by Detecting the Discrepancy of Second Moment Structure of Scaled functions

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KAUST

This talk presents a new classification method for functional data. We consider the case where different groups of functions have similar means so that it is difficult to classify them based on only the mean function. To overcome this limitation, we propose the second moment based functional classifier (SMFC). Here, we demonstrate that the new method is sensitive to divergence in the second moment structure and thus produces lower rate of misclassification compared to other competitor methods. Our method uses the Hilbert-Schmidt norm to measure the divergence of second moment structure. One important innovation of our classification procedure lies in the dimension reduction step. The method data-adaptively discovers the basis functions that best capture the discrepancy between the second moment structures of the groups, rather than uses the functional principal component of each individual group, and good performance can be achieved as unnecessary variability is removed so that the classification accuracy is improved. Consistency properties of the classification procedure and the relevant estimators are established. Simulation study and real data analysis on phoneme and rat brain activity trajectories empirically validate the superiority of the proposed method.

Zhuo Qu

Ph.D. Student, Statistics
Zhuo Qu is a PhD candidate in statistics, under Prof. Marc Genton. Zhuo likes to use statistics methods to interpret the data in the applications and find the new possibility of exploring the new statistics model. Being eager to learn and experience the uncertainty in the research and life, she also like chatting, swimming and piano. Her goal is to be a spare-time pianist among professional statisticians. Education and Early Career King Abdullah University of Science and Technology, Thuwal, January 2019 - December 2022, PhD in Statistics King Abdullah University of Science and Technology

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core
<> Geospatial Data (3)

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B9 L2 R2325

This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.

Geospatial Data Science for Public Health Surveillance - Seminar

Paula Moraga, Lecturer, Department of Mathematical Sciences, University of Bath, UK

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B9 L2 H2

In this talk, I will give an overview of my research which focuses on the development of innovative statistical methods and interactive visualization applications for geospatial data analysis and health surveillance. I will illustrate some of my projects in the following areas: 1. Development of new statistical methodology; 2. Development of open-source statistical software such as the R packages; 3. Health surveillance projects. Finally, I will describe my future research on innovation in data acquisition and visualization, precision disease mapping, and digital health surveillance, and how it can inform policymaking and improve population health globally.

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core
<> haps (3)

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
H

Hussein Al-Shatri

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.
<> high dimensional statistics (3) <> High Performance Computing (3) <> HPC (3)
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

Esmail Abdul Fattah

Ph.D., Statistics

HPC

Introduction I am currently serving as a Postdoctoral Researcher at the Extreme Computing Research Center at KAUST, where I have the privilege of working under the leadership of the center's director, Professor David Keyes. My research journey here is further enriched by the close collaboration and guidance I receive from Dr. Hatem Ltaief, whose expertise significantly contributes to my professional growth and research endeavors in this dynamic field. My academic journey led me to earn a Ph.D. in Statistics under the mentorship of Professor Håvard Rue, working within his esteemed research
<> INLA (3)

Raphaël Huser’s article among the top downloaded articles in Springer’s Environmental Sciences Journals

1 min read ·
Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.

Jonatan A. González

Postdoctoral Research Fellow, Statistics
Jonatan's research interests lie between spatial statistics and coding, emphasising point processes, epidemiology, infectious disease modelling and, generally, application-focused methods. He has a bachelor's degree and a master's background in mathematics. He completed his PhD in spatial and spatio-temporal point processes at the University Jaume I. Jonatan's research projects include techniques for dealing with point patterns: replicated observations, spatio-temporal analysis, graphical methods, hypothesis testing, experimental design and local characteristics. In addition to the core

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics
Dr. Daniela Castro-Camilo was a postdoctoral fellow in the Extreme Statistics Research Group of Prof. Raphaël Huser from November, 2015, to May, 2019, doing research on the theory and applications of multivariate and spatial extremes. After her postdoc at KAUST, Daniela moved to the University of Glasgow, United Kingdom, where she embraced an academic career by becoming a Lecturer (equivalent to Assistant Professor in the UK) of Statistics. See her personal webpage. Education and early career Daniela Castro-Camilo received her Ph.D. from Pontificia Universidad Católica de Chile in August 2015
<> Localization (3) <> multivariate analysis (3) <> numerical methods (3)

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.
<> Photonics and optoelectronics (3) <> proteins (3) <> reliability testing (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> reproducibility issues (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> scientific enterprise (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> solar energy (3)

Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

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B9 H2 R2325

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the

Securing future energy supply

1 min read ·
KAUST’s Earth Science and Engineering Program is underpinned by a drive to develop sustainable ways to get the best from natural resources and quantify environmental hazards.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.
<> spatio-temporal data analysis (3)

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

-

B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Ruiman Zhong

Ph.D. Student, Statistics
Ruiman is a Ph.D. candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Before joining KAUST, Ruiman obtained her Master's Degree in Statistics (August 2020) from Katholieke Universiteit Leuven, Belgium. Her research interests include spatial and spatial-temporal data modeling.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.
<> Spectral Analysis (3)

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

Mara Sherlin Talento

Ph.D. Student, Statistics
We do not gain confidence through measuring what we know, but through quantifying how much we do not know. Mara Sherlin Talento is a PhD student in the Biostatistics group of King Abdullah University of Science and Technology, under the supervision of Prof. Hernando Ombao. She finished her Master’s degree in University of the Philippines and served the chairperson of Young Statisticians Committee of the International Statistical Institute during 2020 – 2023. She is also a member of American Statistical Association and International Biometric Society.

Hernando Ombao

Professor, Statistics
KAUST Professor of Statistics Hernando Ombao is an expert in time series models and their applications to designed medical experiments on complex biological processes.