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Juan José Galeano Arenas is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group.

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

statistics spatio-temporal statistics GPU Computing HPC

Nov 12, 09:00 - 11:00

B5 L5 R5209

This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy.

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

Mehdi Moodaaliat, Assistant Professor, Marquette University

Mar 10, 15:00 - 16:00

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

Xiaohui Chang, Assistant Professor, College of Business at Oregon State University

Nov 9, 15:30 - 16:00

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

Raul Tempone, Professor, Applied Mathematics and Computational Science

May 22, 12:00 - May 28, 12:00

B9 H1 R2322

numerical methods stochastic differential equations statistics

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

Nov 5, 17:15 - 18:15

B2 L5 R5220

spatio-temporal data analysis statistics

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

Edsel A. Peña, iProfessor in the Department of Statistics, University of South Carolina

Mar 14, 16:00 - 17:00

B1 L4 R4102

statistics Bayesian computational statistics inductive inference

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

Nov 14, 12:00 - 13:00

B9 L2 H1 R2322

statistics SPDEs

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

Zhuo Qu, Ph.D. Student, Statistics

1 min read · Wed, Apr 11 2018

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."

student award statistics

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

New stats apps show a virtual reality

statistics visualization computational predictions

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

1 min read · Thu, Oct 24 2019

disaster management Environmental Statistics statistics

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

Yuxiao Li

Ph.D., Statistics

Environmental Statistics 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

statistics High Performance Computing HPC geospatial statistics spatio-temporal statistics

Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.

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

Ph.D., Statistics

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

computational statistics spatio-temporal statistics Environmental Statistics statistics

Jian Cao

Ph.D., Statistics

data science spatio-temporal statistics 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

Bayesian computational statistics statistics geospatial statistics health surveillance spatio-temporal disease data

Haakon Bakka

Postdoctoral Research Fellow (former), Statistics

Bayesian computational statistics 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

Bayesian computational statistics 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

Senior Research Scientist, Statistics

spatio-temporal statistics Environmental Statistics statistics machine learning

Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis.

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.

Hans Montcho

Ph.D. Student, Statistics

Bayesian computational statistics machine learning Bayesian computational technique cross validation point processes statistics Python (Programming Language)

Hans Montcho is a Ph.D. Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST).

Cristian Chiuchiolo

Bayesian computational statistics statistics

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

Bushra Bin Hemid

M.S. Student

artificial intelligence machine learning information security privacy preserving techniques

MSc/PhD candidate in Artificial Intelligence at KAUST. Expertise in AI and information security. Research focuses on AI for infrared imaging, transforming sensing technologies for healthcare, agriculture, and smart infrastructure

Echo Ziyi Yang

Ph.D. Student

Autonomous Agent machine learning Reinforcement Learning

Event Status: Cancelled | Remote Sensing and Agroinformatics Insights in Saudi Arabia Using Machine Learning

Ting Li, Postdoctoral Research Fellow, Environmental Science and Engineering

Mar 5, 12:00 - 13:00

B9 L2 R2325

remote sensing machine learning sustainable agricultural agricultural productivity

This talk explores how machine learning and high-resolution satellite remote sensing are being used to transform vast amounts of raw data into actionable agroinformatics at a national scale, providing the precision needed to manage these vital resources sustainably.

Ting Li

Postdoctoral Research Fellow, Environmental Science and Engineering

remote sensing machine learning precision agriculture agriculture environmental modeling crop health agricultural productivity sustainable agricultural

Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations

AI machine learning deep learning

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

Racha Zayni

Visiting Researcher, Information Science Lab

machine learning Data Analytics Wireless Communications AI-based Localization Signal processing

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

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read · Wed, Jan 21 2026

optimization machine learning communication efficiency artificial intelligence big data algorithm

The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python (Programming Language)

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Jonathan Klein

Research Scientist, Computer Science

machine learning Computational Photography computational simulations

My fields of research interests include computational simulation, plant simulation and modeling, synthetic training data, machine learning architectures, none-line-of-sight imaging, and computational photography.

KAUST Workshop on Distributed Training in the Era of Large Models

Nov 24 - 26, All day

Auditorium between B4 & 5, L0, R0215

machine learning Distributed algorithms generative models ML

Join leading researchers and innovators to explore how distributed training is reshaping the next generation of large-scale AI models.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

Arto Maranjyan, Ph.D. Student, Computer Science

Nov 13, 12:00 - 13:00

B9 L2 R2325

machine learning optimization asynchronous algorithms Training

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

Yibin Wang

machine learning Free Space Optics FPGA

Keep Moving forward

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Sep 4, 12:00 - 13:00

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

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

machine learning Geospatial Data GPU Computing

May 8, 12:00 - 13:00

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

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

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

Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures

Mar 13, 12:00 - 13:00

B9 L2 R2325

petsc PETScML machine learning

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 (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Research Scientist (former), King Abdullah University of Science and Technology

6g wireless systems cloud- and fog-radio access networks cross-layer optimization Cooperative networks machine learning Optical communications

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

Karen Sanchez

Postdoctoral Research Fellow

Computer Vision machine learning AI for healthcare deep learning Medical imaging

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

machine learning Computer Vision AI4Science

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

Computer Vision machine learning Deep generative models

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

Visiting Scholar (former), King Abdullah University of Science and Technology

Signal processing Wireless Communications machine learning

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

Muhammad Esmat

Visiting Scholar (former), King Abdullah University of Science and Technology

machine learning neural network

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

Yasser Jaber

Visiting Researcher (former), King Abdullah University of Science and Technology

machine learning software artificial intelligence

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

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

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

Abdulkarem Badiea Alshaibani

M.S. Student, Information Science Lab

Localization machine learning GNSS localization Riemannian Optimization

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

Reproducibility in Computer Science - Part 1

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 4, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 5, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 6, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

applied statistics machine learning Gaussian processes

Oct 3, 12:00 - 13:00

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 · Thu, Dec 8 2016

big data machine learning knowledge discovery supercomputing

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 · Mon, Mar 13 2017

machine learning big data artificial intelligence comparative genomics biomedical data human health

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 · Tue, Oct 24 2017

machine learning big data cloud computing

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 · Sun, Oct 14 2018

bioinformatics machine learning computational models

Xin Gao’s research is building bridges between computer science and biology.

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.''

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.

Zainab A. Homoud

M.S. Student, Bioengineering

machine learning bioinformatics 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

Professor, Computer Science

bioinformatics Computational biology machine learning big data

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

STSDS smart grids machine learning big data

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

machine learning optimization statistics big data

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

Senior Research Scientist, Applied Mathematics and Computational Science

exageostat extreme computing parallel algorithms large-scale computing machine learning Statistical computing

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

Statistics of extremes extreme-value theory spatio-temporal statistics data science machine learning copulas Environmental Statistics geostatistics applications to finance applications to neuroscience

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

spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational 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

spatio-temporal statistics Statistics of extremes machine learning Deep learning

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 and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. 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

Jesper Tegnér

Professor, Bioscience

living systems machine learning machine intelligence systems medicine genomics artificial intelligence brain-inspired computation Systems neuroscience algorithmic information theory Dynamical Systems spatial transcriptomics single cell biology

Jasper Tegnér 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

Senior Research Scientist, Statistics

spatio-temporal statistics Environmental Statistics statistics machine learning

Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis.

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

MCMC Particle Methods Stochastic Control machine learning stochastic partial differential equations

Hans Montcho

Ph.D. Student, Statistics

Bayesian computational statistics machine learning Bayesian computational technique cross validation point processes statistics Python (Programming Language)

Hans Montcho is a Ph.D. Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST).

Di Wang

Assistant Professor, Computer Science

machine learning data mining privacy security

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

M.S., Statistics

machine learning Deep learning applied statistics

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

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

3 min read · Tue, Apr 22 2025

spatio-temporal statistics Environmental Statistics computational statistics

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

Oct 4, 09:45 - Oct 6, 10:45

B1 L4 R4102

Statistics of extremes Environmental Statistics

Workshop on Computational Space-Time Statistics

Uncertainty Quantification of Tsunami Models

Serge Guillas, Professor of Statistics, University College London (UCL)

1 min read · Mon, Apr 2 2018

Statistics of extremes Environmental Statistics spatial statistics

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

Statistics of extremes Environmental Statistics spatial 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

Environmental Statistics 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

Environmental Statistics spatial varying coefficients regression

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

Environmental Statistics Space- time Models Statistical Methods for Imaging Data

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

Statistics of extremes Environmental Statistics STSDS

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

Statistics of extremes extreme-value theory spatio-temporal statistics data science machine learning copulas Environmental Statistics geostatistics applications to finance applications to neuroscience

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

Geomorphology landslide susceptibility modeling environmental modeling Environmental 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

Environmental 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

computational statistics spatio-temporal statistics Environmental Statistics statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Environmental 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

Environmental 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

Environmental Statistics spatial 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

Senior Research Scientist, Statistics

spatio-temporal statistics Environmental Statistics statistics machine learning

Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis.

Daniela Castro-Camilo

Postdoctoral Research Fellow, Statistics

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational statistics INLA

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

functional data analysis Quantile spectral methods Functional Time Series forecasting Statistical Modeling Environmental 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

Environmental Statistics High dimensional Time Series computational 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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

geospatial statistics spatio-temporal statistics High Performance Computing HPC multivariate statistics GPU Algorithms

May 14, 15:00 - 17:00

B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

Yang Xiao

Ph.D. Student, Statistics

spatio-temporal statistics bayesian inference Public Health Adaptive modeling forecasting Python (Programming Language)

Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.

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

statistics spatio-temporal statistics GPU Computing HPC

Nov 12, 09:00 - 11:00

B5 L5 R5209

This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy.

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

3 min read · Tue, Apr 22 2025

spatio-temporal statistics Environmental Statistics computational statistics

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

Spatio-Temporal Statistics in Geo-Environmental Data Science

Ying Sun, Professor, Statistics

Nov 26, 15:00 - 16:30

B9, L2, R2322

spatio-temporal statistics

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

Mar 12, 12:00 - 13:00

KAUST

functional data analysis spatio-temporal statistics

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

1 min read · Wed, Oct 13 2021

spatio-temporal statistics Deep learning

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.

Ph.D. Student, Statistics

spatial statistics extreme-value theory spatio-temporal statistics computational 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

spatio-temporal statistics Statistics of extremes machine learning Deep learning

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 and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. 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

Mary Lai O. Salvana

Ph.D., Statistics

computational statistics spatio-temporal statistics Environmental Statistics statistics

Jose Hermenegildo Ramirez Gonzalez

Postdoctoral Research Fellow, Statistics

Jonatan A. González

Postdoctoral Research Fellow, Statistics

spatial statistics Point patterns Functional data R project INLA spatio-temporal statistics Geospatial Data

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

data science spatio-temporal statistics 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

Senior Research Scientist, Statistics

spatio-temporal statistics Environmental Statistics statistics machine learning

Harrou’s research focuses on statistical decision theory and its applications, multivariate statistical process monitoring, anomaly detection and diagnosis.

Elias Teixeira Krainski

Research Scientist, Statistics

Bayesian computational statistics geospatial statistics spatio-temporal statistics

Elias' research focuses on efficient Bayesian methods for practical implementation, spatio and spatio-temporal statistics, INLA.

Daniela Cisneros

Ph.D. Student, Statistics

Statistics of extremes spatio-temporal 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

Amin Wu

Ph.D., Statistics

spatio-temporal statistics Gaussian processes

Amanda Lenzi

Postdoctoral Research Fellow, Statistics

spatio-temporal statistics Bayesian 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

The Role of Humans in Scientific Discovery in the Age of LLMs — Beyond Asking: Turning LLMs into Research Collaborators

Sir Bashir M. Al-Hashimi, Vice President, Research & Innovation, King’s College London (KCL); Distinguished Professor, Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King’s College London (KCL)

Apr 8, 12:00 - 14:15

B9 R2325

AI artificial intelligence LLM scientific research scientific knowledge Assistive Technology

Rather than offering definitive conclusions, this talk seeks to stimulate dialogue, question assumptions, and inspire new forms of collective thinking about the future of scientific research and doctoral training in an AI-driven world.

Bushra Bin Hemid

M.S. Student

artificial intelligence machine learning information security privacy preserving techniques

MSc/PhD candidate in Artificial Intelligence at KAUST. Expertise in AI and information security. Research focuses on AI for infrared imaging, transforming sensing technologies for healthcare, agriculture, and smart infrastructure

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read · Tue, Feb 10 2026

News Clip News Press Releases

artificial intelligence generative ai Computer Vision Sports

KAUST has been designated the first FIFA Research Institute in the Middle East and Asia, and the fifth worldwide, reflecting the University’s commitment to advancing innovation in football through cutting-edge scientific inquiry.

Data-driven Anomaly Detection in Industrial Processes

Fouzi Harrou, Senior Research Scientist, Statistics

Feb 12, 12:00 - 13:00

B9 L2 R2325

anomaly detection multivariate statistics artificial intelligence AI

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read · Wed, Jan 21 2026

optimization machine learning communication efficiency artificial intelligence big data algorithm

The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Nuclear Fusion Powered by AI and HPC

Vladimir Pimanov, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Dec 4, 12:00 - 13:00

B9 L2 R2325

AI artificial intelligence Fusion simulation

This talk presents advanced simulations with AI-driven optimization to improve the performance of a next-generation plasma-jet-driven magneto-inertial fusion concept.

Salem Al-saqaf

IoT electronics artificial intelligence integrated circuits

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

Deep learning Generative AI and LLMs artificial intelligence Computer Vision

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science

artificial intelligence Generative AI and LLMs

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 Researcher (former), King Abdullah University of Science and Technology

machine learning software artificial intelligence

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

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab

artificial intelligence TinyML optimization deep learning

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 4, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 5, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 6, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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 · Wed, May 27 2015

semantic analysis artificial intelligence UAV

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 · Mon, Mar 13 2017

machine learning big data artificial intelligence comparative genomics biomedical data human health

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 · Sun, Aug 6 2017

big data artificial intelligence computational methods health protein structures biology

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 · Mon, Nov 12 2018

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).

artificial intelligence

Tweaking tools to track tweets over time

1 min read · Sun, Feb 17 2019

Social media data machine learning artificial intelligence

Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

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 · Tue, Jun 9 2020

artificial intelligence COVID-19

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 · Tue, Jul 28 2020

COVID-19 artificial intelligence

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 · Tue, Sep 15 2020

machine learning artificial intelligence

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 · Sun, Nov 22 2020

machine learning artificial intelligence statistics human health

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 · Mon, Jun 28 2021

digital innovation award smart tap artificial intelligence

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 · Mon, Nov 29 2021

IoT agriculture artificial intelligence drones

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

computational statistics Time Series Data Mining artificial intelligence

Pratik Nag

Ph.D. Student, Statistics

spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational 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 Tegnér

Professor, Bioscience

living systems machine learning machine intelligence systems medicine genomics artificial intelligence brain-inspired computation Systems neuroscience algorithmic information theory Dynamical Systems spatial transcriptomics single cell biology

Jasper Tegnér 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.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read · Wed, Jan 21 2026

optimization machine learning communication efficiency artificial intelligence big data algorithm

The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

Prof. Daniel Peña Sánchez de Rivera, Department of Statistics, Universidad Carlos III de Madrid

Professor, Computer Science

bioinformatics Computational biology machine learning big data

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

STSDS smart grids machine learning big data

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

machine learning optimization statistics big data

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.

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

Apr 30, 13:00 - 16:00

B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations

AI machine learning deep learning

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

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python (Programming Language)

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Zakaria El Kassimi

machine learning deep learning Large Language Models

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Adonai Vera

Intern, Image and Video Understanding Lab

Computer Vision Advanced Perception multimodal AI deep learning

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

Ph.D. Student, Applied Mathematics and Computational Science

applied mathematics bioscience Dynamical Systems deep learning bifurcation theory

Karen Sanchez

Postdoctoral Research Fellow

Computer Vision machine learning AI for healthcare deep learning Medical imaging

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

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Sixiu Liu

Ph.D. (former), Earth Science and Engineering

seismic processing deep learning

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

artificial intelligence TinyML optimization deep learning

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

Xiang Chen

Ph.D. Student, Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Mapping risk on networks: A scalable model for spatial point processes earns international recognition

2 min read · Mon, Jul 28 2025

spatial statistics Gaussian processes R-INLA

KAUST Ph.D. student Damilya Saduakhas wins Best Paper Award for a new model mapping accident risk on complex road networks.

Kriging Asymptotics

William Kleiber, Assistant Professor, University of Colorado

Nov 9, 15:00 - 16:30

B1 L4 R4102

spatial statistics

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

Dorit Hammerling, National Center for Atmospheric Research (NCAR)

Feb 10, 15:30 - 16:30

B1 L4 R4102

spatial statistics

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

Mar 13, 09:00 - Mar 15, 15:00

B1 L4 R4102

Environmental Statistics spatial statistics

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

Dr. Luigi Lombardo, University of Twente, Netherlands

May 14, 16:00 - 17:00

B1 L4 R4102

University of Twente spatial statistics

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

spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

Apr 5, 15:00 - 17:00

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

Zhongwei Zhang, Ph.D. Student, Statistics

M.S. Student, Statistics

Postdoctoral Research Fellow, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Kelvin J. R. Almeida-Sousa is a Brazilian mathematician with contributions to deterministic and stochastic partial differential equations, numerical analysis, mathematical statistics, and spectral theory.

Joydeep Chowdhury

Postdoctoral Research Fellow, Statistics

functional data analysis multivariate analysis spatial statistics quantile regression non-parametric methods

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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

spatial statistics Point patterns Functional data R project INLA spatio-temporal statistics Geospatial Data

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

Bayesian computational statistics bayesian methodology latent Gaussian models spatial statistics

Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gaurav Agarwal

Ph.D., Statistics

Environmental Statistics spatial 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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational statistics INLA

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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Alexandre Simas is an expert on stochastic processes and spatial statistics, with a focus on stochastic partial differential equations and random fields on metric graphs.

Rare-Event Simulation Methods for Outage Probability in GSC/MRC Systems under Rician Fading

Mahmoud Hassan Ghazal , Ph.D. Student, Electrical and Computer Engineering

Apr 23, 12:00 - 13:00

B9 R2325

Statistics of extremes Outage Probability Applied Probability

This talk explores enhanced Monte Carlo (MC) techniques for estimating the OP of SIMO systems under Rician fading, where the selected signals are combined using maximum ratio combining (GSC/MRC).

Simon Nik

Postdoctoral Research Fellow, Statistics

Statistics of extremes extreme-value theory stochastic partial differential equations discrete time series

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

Oct 4, 09:45 - Oct 6, 10:45

B1 L4 R4102

Statistics of extremes Environmental Statistics

Workshop on Computational Space-Time Statistics

Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

Zhongwei Zhang, Ph.D. Student, Statistics

1 min read · Tue, Sep 3 2019

Ph.D. Student, Statistics

Statistics of extremes Environmental Statistics STSDS

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

Statistics of extremes extreme-value theory spatio-temporal statistics data science machine learning copulas Environmental Statistics geostatistics applications to finance applications to neuroscience

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

Statistics of extremes spatial statistics data science

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

spatio-temporal statistics Statistics of extremes machine learning Deep learning

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 and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. 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

Jordan Richards

Postdoctoral Research Fellow, Statistics

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Statistics of extremes

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

Statistics of extremes spatio-temporal 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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational statistics INLA

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

copulas Statistics of extremes nonparametrics

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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Reproducibility in Computer Science - Part 1

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 4, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

Nov 5, 16:00 - 17:00

B9 L3 R3223

reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

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

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

1 min read · Sun, Nov 24 2019

algorithms machine learning Computer science

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 · Wed, Oct 30 2019

artificial intelligence Computer science

A deep learning tool could help in structure-based drug discovery.

Training agents to walk with purpose

1 min read · Sun, May 31 2020

statistics big data Computer science

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 · Wed, Jun 10 2020

communications Computer science

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

Computers excel in chemistry class

1 min read · Mon, Aug 24 2020

Computer science combustion machine learning

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 · Wed, Jun 9 2021

big data Computer science

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 · Mon, Jul 12 2021

machine learning algorithm modeling Computer science

Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

New database to "SpUR" on cancer research

1 min read · Sun, May 29 2022

human health cancer Computer science

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 · Sun, May 29 2022

human health Imaging machine learning Computer science

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

On Cross Validation, Log Gaussian Cox Process and Variational Bayes

Hans Montcho, Ph.D. Student, Statistics

Apr 29, 10:00 - 12:00

B2 R5220; Zoom Meeting 99219689653

Bayesian computational technique Bayesian computational statistics latent Gaussian models Point patterns spital point patterns

This dissertation advances Bayesian computation by developing a cross validation approach to assess LGCPs defined on Euclidean, manifolds or network domains and improving skewed posterior approximations for LGMs.

Haziq Jamil

Research Specialist, Statistics

Statistical methodology Bayesian computational statistics Latent variable models Structural equation models Item Response Theory

Haziq Jamil is a Research Specialist at the King Abdullah University of Science and Technology. His research focuses on statistical theory, methods and computation, with a special inclination towards social science applications.

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

Edsel A. Peña, iProfessor in the Department of Statistics, University of South Carolina

Mar 14, 16:00 - 17:00

B1 L4 R4102

statistics Bayesian computational statistics inductive inference

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

Hernando Ombao, Professor, Statistics

Nov 5, 12:00 - 13:00

KAUST

Linear Multivariate extremes Bayesian computational statistics

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

Cristian Chiuchiolo, Ph.D. Student, Electrical and Computer Engineering

Jun 6, 15:00 - 17:00

B3 L5 R5209

Bayesian computational statistics Bayesian Statistics

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

Mar 6, 12:00 - 13:00

B9 L3 R3128

Bayesian computational statistics Deep learning

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

Jun 4, 15:00 - 16:00

B4 L5 R5220

Bayesian computational statistics

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 · Sun, Oct 4 2015

statistics spatio-temporal statistics Bayesian computational statistics

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 · Wed, Sep 19 2018

statistics Bayesian computational statistics healthcare ecology

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 · Sun, Aug 21 2022

Bayesian computational statistics

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

Bayesian computational statistics data analysis

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

Małgorzata Forystek

M.S. Student, Statistics

Bayesian computational statistics data science

Statistics Master's student interested in Data Science

Janet van Niekerk

Research Scientist, Statistics

Bayesian computational statistics Survival analysis Biostatistics

Hanan Alahmadi

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.

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

Alfio Quarteroni, Emeritus Professor, Politecnico di Milano and EPFL

Jan 25, 14:00 - 15:00

B9, L2, R2322

Computational mathematics numerical methods Scientific Machine Learning scientific computing applied mathematics

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

Juan Pablo Muñoz Díaz

Ph.D. Student, Applied Mathematics and Computational Science

applied mathematics bioscience Dynamical Systems deep learning bifurcation theory

1 min read · Thu, May 18 2017

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

applied mathematics data

A clique away from more efficient networks

1 min read · Sun, Jun 14 2020

networks connectivity applied mathematics electrical engineering

An old branch of mathematics finds a fertile new field of application.

Unleashing the potential of tethered drones

1 min read · Tue, Jul 14 2020

electrical engineering applied mathematics

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

Thomas Trigo Trindade

Research Consultant, Stochastic Numerics Research Group

uncertainty quantification numerical analysis data assimilation

Abdul-Lateef Haji-Ali

Ph.D., Applied Mathematics and Computational Science

uncertainty quantification numerical analysis Multilevel Monte Carlo

Shyam Mohan Subbiah Pillai

Research Consultant, Stochastic Numerics Research Group

numerical analysis uncertainty quantification stochastic differential equations

Nabila Bounceur

Research Scientist, Stochastic Numerics Research Group

probabilistic sensitivity analysis statistical emulators uncertainty quantification complex system modeling

My work focuses on the modeling of complex systems from the Probabilistic Sensitivity Analysis (PSA) and Uncertainty Quantification (UQ) perspective. I develop methodologies to efficiently characterize input-output relationships in computationally expensive simulators, enabling both qualitative understanding (which factors matter most?) and quantitative assessment (how much does each factor contribute to output uncertainty?).

Abderrahmene Ben Romdhane

M.S. Student, Stochastic Numerics Research Group

numerical analysis Computational finance uncertainty quantification

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis uncertainty quantification optimal experimental design

Uncertainty-Aware Learning: From Bayesian Neural Networks to Agentic Decision Making

Theodore Papamarkou, Founder, PolyShape; Visiting Professor, School of Applied Mathematical and Physical Sciences (SEMFE), National Technical University of Athens (NTUA)

Feb 1, 12:00 - 13:00

B4/5 L0 A0215

uncertainty quantification neural network Bayesian modeling AI

This talk points out that uncertainty quantification is important for reliable AI, and that modern machine learning should be viewed through the lens of probabilistic decision making.

Amal Alghamdi

HPC Python (Programming Language) data analysis finite element method uncertainty quantification

ACM Gordon Bell Prize for Climate Modelling

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

1 min read · Fri, Nov 22 2024

News Clip News

HPC climate projections extreme computing computational simulations uncertainty quantification

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

Serge Guillas, Professor of Statistics, University College London (UCL)

Sep 8, 15:00 - 16:00

B1

uncertainty quantification Environmental Statistics

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 · Thu, Jul 24 2014

uncertainty quantification stochastic differential equations numerical methods

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 · Wed, Mar 4 2015

uncertainty quantification earthquake source modelling

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 · Thu, May 30 2019

Bayesian Estimation Geophysical Fluid Modeling uncertainty quantification

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 · Sun, Jul 7 2019

geophysical fluid systems Bayesian Estimation uncertainty quantification

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 · Sun, Oct 6 2019

uncertainty quantification bayesian inference computational fluids mechanics

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, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide 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

spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational 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

Dean, Computer, Electrical and Mathematical Sciences and Engineering

uncertainty quantification bayesian inference computational fluids mechanics combustion High Performance Computing

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.

Modeling Complex Dependence in Multivariate Time Series, with Applications to Brain Signals

May 5, 15:00 - 17:00

extreme statistics Biostatistics brain signals multivariate statistics Time Series

This thesis develops approaches to address issues and limitations associated with multivariate processes generated from unknown complex systems such as brain signals.

Time Series Clustering: Pattern Recognition, Forecasting, and Amortized Inference

Ángel López Oriona, Postdoctoral Research Fellow, Statistics

May 14, 12:00 - 13:00

B9 R2325

Time Series Pattern Recognition forecasting statistical inference

This talk presents innovative time series clustering techniques, highlighting a quantile-based approach for analyzing locally stationary data, a predictive framework for enhanced forecasting, and the use of amortized inference to overcome traditional algorithmic limitations.

Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

Prof. Daniel Peña Sánchez de Rivera, Department of Statistics, Universidad Carlos III de Madrid

Apr 25, 16:00 - 17:00

B1 L4 R4102

Time Series big data

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

Time Series

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

Time Series Survival analysis Regression Models Applied Probability

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

Time Series

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

Postdoctoral Research Fellow, Statistics

Time Series neuroscience data analysis brain signals Generative Adversarial Networks topological ranking

Building bridges between mathematical statistics and real-world impact

4 min read · Tue, Jan 6 2026

Spotlight News Research in Focus

mathematical statistics random fields stochastic partial differential equations computational statistics

A research journey that connects statistical theory with the complexity of real-world systems.

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

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

3 min read · Tue, Apr 22 2025

spatio-temporal statistics Environmental Statistics computational statistics

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

Jun 13, 09:00 - 10:00

B1 L4 R4102

computational statistics Environmental Statistics

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

Fangyuan Yu, Ph.D. Student, Statistics

Mar 7, 15:00 - 17:00

KAUST

Monte carlo methods computational statistics Markov models

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

computational statistics Time Series Data Mining artificial intelligence

Ying Sun

Professor, Statistics

spatio-temporal statistics environmental applications computational 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, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide 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

spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational 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

spatial statistics extreme-value theory spatio-temporal statistics computational 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

computational statistics spatio-temporal statistics Environmental Statistics statistics

Jordan Richards

Postdoctoral Research Fellow, Statistics

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

mathematical statistics random fields stochastic partial differential equations computational statistics stochastic processes

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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational statistics INLA

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

Environmental Statistics High dimensional Time Series computational 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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Fully Decentralized Inference for Spatial Data Using Low-Rank Models

Jianwei Shi, Postdoctoral Research Fellow, Statistics

May 7, 12:00 - 13:00

B9 R2325

statistical inference decentralized systems Geospatial Data geospatial statistics optimization

This talk introduces a novel, fully decentralized optimization framework to enable scalable parameter inference in large spatial low-rank models, supported by both theoretical guarantees and empirical validation.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read · Wed, Jan 21 2026

optimization machine learning communication efficiency artificial intelligence big data algorithm

The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

Arto Maranjyan, Ph.D. Student, Computer Science

Nov 13, 12:00 - 13:00

B9 L2 R2325

machine learning optimization asynchronous algorithms Training

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Sep 4, 12:00 - 13:00

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Chengjie Zhao

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Wireless Communications optimization Performance analysis network security

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

artificial intelligence TinyML optimization deep learning

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

May 2, 12:00 - 13:00

B9 L2 H1

optimization DLMC DLMCIS

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

Xin Gao, Professor, Computer Science

Apr 16, 12:00 - 13:00

KAUST

transcription factor optimization Deep learning

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 · Mon, Apr 9 2018

energy chemical science optimization

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

Accelerating the grapevine effect

1 min read · Sun, Jul 7 2019

optimization machine learning big data

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 · Sun, Sep 15 2019

optimization machine learning big data

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.

Xiaotian Jin

Ph.D. Student, Statistics

Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization

Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.

Samuel Horváth

Ph.D. Student, Statistics

machine learning optimization statistics big data

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.

Reem Alzahrani

Ph.D. Student

Deep learning nlp Computer Vision Large Language Models vision language models Multi-Modal Large Language Models

Reem Alzahrani is an MS/PhD student at KAUST, working on Large Language Models and Vision-Language Models. Her research focuses on identifying and addressing the limitations of AI systems to improve their robustness and reliability.

Ansar Khangeldin

M.S. Student

Computer Vision Reinforcement Learning Multi-agent systems autonomous systems

Ms/PhD Electrical and Computer Engineering student. Have a background in Applied Mathematics, Control Theory, Machine Learning Research and Development, Multi-Agent Reinforcement Learning. Main research interests are application and enhancement of CV methods in autonomous systems (AVs, Robotics, UAVs).

Drishya Karki

Intern

Computer Vision Temporal Activity Localization Multi-Modal Learning representation learning Graph Learning

Research Intern working under the supervision of Dr. Silvio Giancola as a part of KAUST-FIFA Research Institute.

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read · Tue, Feb 10 2026

News Clip News Press Releases

artificial intelligence generative ai Computer Vision Sports

KAUST has been designated the first FIFA Research Institute in the Middle East and Asia, and the fifth worldwide, reflecting the University’s commitment to advancing innovation in football through cutting-edge scientific inquiry.

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Dalal E. Aldossary

M.S. Student, Computer Science

machine learning Electroencephalograms Computer Vision Computational Neuroscience

Dalal Aldossary is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Adonai Vera

Intern, Image and Video Understanding Lab

Computer Vision Advanced Perception multimodal AI deep learning

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

Computer Vision Diffusion Models Vision Foundation Models Multi-Modal Large Language Models

Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

Deep learning Generative AI and LLMs artificial intelligence Computer Vision

Karen Sanchez

Postdoctoral Research Fellow

Computer Vision machine learning AI for healthcare deep learning Medical imaging

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

AI Deep learning Computer Vision Reinforcement Learning

Hani A. Al Majed

M.S. Student

machine learning Computer Vision AI4Science

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

Computer Vision machine learning Deep generative models

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.

2018 Workshop on Statistics and Data Science

Nov 12, 08:30 - Nov 15, 17:00

Auditorium 0215 between Buildings 4 & 5

data science high dimensional statistics modeling biological processes physical processes

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

Nov 30, 16:30 - 17:30

KAUST

statistical inference data science

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

Oct 31, 09:00 - Nov 3, 17:00

B2 B3 A0215

statistics data science big data modeling complex data

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

Nov 6, 09:00 - Nov 9, 17:00

B18 R4203

statistics data science modeling big data sustainability health environment

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 · Mon, Jun 11 2018

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.

WiDS data science

Alumni Feature: Majed Alzahrani

1 min read · Sun, Oct 7 2018

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.

data science

2019 Statistics and Data Science Workshop

1 min read · Wed, Dec 11 2019

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.

data science statistics

KAUST STAT students come together for second ASA chapter meeting

1 min read · Thu, Dec 3 2020

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.

statistics data science

Raphaël Huser

Associate Professor, Statistics

Statistics of extremes extreme-value theory spatio-temporal statistics data science machine learning copulas Environmental Statistics geostatistics applications to finance applications to neuroscience

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

Statistics of extremes spatial statistics data science

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

Bayesian computational statistics data science

Statistics Master's student interested in Data Science

Jian Cao

Ph.D., Statistics

data science spatio-temporal statistics 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

Fernando Rodriguez Avellaneda

Ph.D. Student, Statistics

spatio-temporal modeling environmental research Spatial epidemiology Python (Programming Language) data science

Fernando Rodriguez Avellaneda's research focuses on Bayesian spatial and spatio-temporal modeling, with applications in environmental and epidemiological studies.

Eman Kabbas

Ph.D. Student, Applied Mathematics and Computational Science

Bayesian and computational Statistics Bayesian Data Aalysis data science Applied and theoretical statistics Data Sciences

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.

Houcem Ben Salem

Intern (former), Information Science Lab

mathematics modeling Programming language

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

2018 Workshop on Statistics and Data Science

Nov 12, 08:30 - Nov 15, 17:00

Auditorium 0215 between Buildings 4 & 5

data science high dimensional statistics modeling biological processes physical processes

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

Diogo Gomes, Professor, Applied Mathematics and Computational Science

Nov 7, 12:00 - 13:00

B9 L2 H1 R2322

mean-field games price formation modeling linear-quadratic problem optimal transport with constraints supply and demand balance

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

Oct 31, 09:00 - Nov 3, 17:00

B2 B3 A0215

statistics data science big data modeling complex data

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

Nov 6, 09:00 - Nov 9, 17:00

B18 R4203

statistics data science modeling big data sustainability health environment

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 · Sun, Jul 30 2017

earth science and engineering climatology Red Sea modeling

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

underwater sensor platform electrical engineering communications

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 · Wed, Apr 17 2019

communications electrical engineering sensors

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 · Thu, Dec 12 2019

communications electrical engineering IoT

Hovering airborne vehicles could connect smart sensors to the internet of things.

This talk introduces a novel, fully decentralized optimization framework to enable scalable parameter inference in large spatial low-rank models, supported by both theoretical guarantees and empirical validation.

Jianwei Shi

Postdoctoral Research Fellow, Statistics

distributed computing decentralized systems Statistical Modelling geospatial statistics geospatial modeling

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

Apr 30, 13:00 - 16:00

B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Geospatial Data Science for Public Health Surveillance

Geospatial Data Public Health Public health surveillance geospatial statistics statistical methods

Mar 9, 11:15 - 12:45

B4/5 L0 A0215; Zoom Meeting 94713495879

This talk presents an overview of our research on innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and how this work has directly informed strategic policy to reduce disease burden.

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

Daniela Cisneros, Ph.D. Student, Statistics

Sep 11, 16:00 - 17:00

B3 L5 R5220

extreme statistics Applied Machine Learning geospatial statistics

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 · Tue, Aug 23 2022

statistics geospatial statistics health surveillance

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

Xiran Zhang

Ph.D. Student, Statistics

statistics High Performance Computing HPC geospatial statistics spatio-temporal statistics

Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.

Xiang Chen

Ph.D. Student, Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Hanan Alahmadi

Ph.D. Student, Statistics

Bayesian computational statistics statistics geospatial statistics health surveillance spatio-temporal disease data

Elias Teixeira Krainski

Research Scientist, Statistics

Bayesian computational statistics geospatial statistics spatio-temporal statistics

Elias' research focuses on efficient Bayesian methods for practical implementation, spatio and spatio-temporal statistics, INLA.

Damilya Saduakhas

Ph.D. Student, Statistics

geospatial statistics environmental applications multivariate spatial processes

Salem Al-saqaf

IoT electronics artificial intelligence integrated circuits

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Serine Medjber

Visiting Student (former), King Abdullah University of Science and Technology

Signal processing embedded systems IoT

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

Mohammed Amine Labbas

Visiting Student (former), King Abdullah University of Science and Technology

IoT Signal processing wireless communications. embedded systems

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

Ruochen Zhu

human body communication IoT

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

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

5G IoT Networks: from Massive access to URLLC

Prof. Yonghui Li, Director of Wireless Engineering Laboratory, The University of Sydney

1 min read · Thu, Jun 13 2019

Red Sea environment marine science earth science and engineering

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

Thomas Trigo Trindade

Research Consultant, Stochastic Numerics Research Group

uncertainty quantification numerical analysis data assimilation

Statistical Modeling of Financial Extremes and Volatility Dynamics

Junshu Jiang, Ph.D. Student, Statistics

Apr 6, 14:00 - 17:00

B2 R5220

Quantitative finance Statistical Modeling extreme events numerical analysis

This thesis provides comprehensive statistical tools for understanding and modeling extreme risks and volatility dynamics in financial markets.

Lajos Lóczi

Research Scientist, Applied Mathematics and Computational Science

numerical methods PDEs ODEs stability analysis numerical discretization qualitative theory numerical analysis

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

Abdul-Lateef Haji-Ali

Ph.D., Applied Mathematics and Computational Science

uncertainty quantification numerical analysis Multilevel Monte Carlo

Shyam Mohan Subbiah Pillai

Research Consultant, Stochastic Numerics Research Group

numerical analysis uncertainty quantification stochastic differential equations

Abderrahmene Ben Romdhane

M.S. Student, Stochastic Numerics Research Group

numerical analysis Computational finance uncertainty quantification

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis uncertainty quantification optimal experimental design

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

solid mechanics biomechanics numerical analysis scientific computing

Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

A shock to the system

1 min read · Wed, Jul 22 2020

Spectral Analysis numerical analysis COVID-19 research

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 (former), Statistics

Stochastic fractional differential equations Metric graphs numerical analysis optimal control problems

Vaibhav Mehandiratta was a Postdoctoral Research Fellow in the CEMSE Division, Statistics Program, at the King Abdullah University of Science and Technology (KAUST), supervised by Prof. David Bolin. He received his Ph.D. in Mathematics from the Indian Institute of Technology Delhi in November 2022 and joined KAUST as a Postdoctoral Fellow in January 2023. He held this position until September 2024. He is currently an Assistant Professor in the Department of Mathematics at BITS Pilani, K K Birla Goa Campus, India.

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide 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.

Junshu Jiang

Ph.D. Student, Statistics

Quantitative finance Statistical Modelling extreme events numerical analysis qualitative research Linux Python (Programming Language)

The Role of Humans in Scientific Discovery in the Age of LLMs — Beyond Asking: Turning LLMs into Research Collaborators

Sir Bashir M. Al-Hashimi, Vice President, Research & Innovation, King’s College London (KCL); Distinguished Professor, Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King’s College London (KCL)

Apr 8, 12:00 - 14:15

B9 R2325

AI artificial intelligence LLM scientific research scientific knowledge Assistive Technology

Rather than offering definitive conclusions, this talk seeks to stimulate dialogue, question assumptions, and inspire new forms of collective thinking about the future of scientific research and doctoral training in an AI-driven world.

Data-driven Anomaly Detection in Industrial Processes

Fouzi Harrou, Senior Research Scientist, Statistics

Feb 12, 12:00 - 13:00

B9 L2 R2325

anomaly detection multivariate statistics artificial intelligence AI

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations

AI machine learning deep learning

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

Uncertainty-Aware Learning: From Bayesian Neural Networks to Agentic Decision Making

Theodore Papamarkou, Founder, PolyShape; Visiting Professor, School of Applied Mathematical and Physical Sciences (SEMFE), National Technical University of Athens (NTUA)

Feb 1, 12:00 - 13:00

B4/5 L0 A0215

uncertainty quantification neural network Bayesian modeling AI

This talk points out that uncertainty quantification is important for reliable AI, and that modern machine learning should be viewed through the lens of probabilistic decision making.

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Nuclear Fusion Powered by AI and HPC

Vladimir Pimanov, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Dec 4, 12:00 - 13:00

B9 L2 R2325

AI artificial intelligence Fusion simulation

This talk presents advanced simulations with AI-driven optimization to improve the performance of a next-generation plasma-jet-driven magneto-inertial fusion concept.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Sep 4, 12:00 - 13:00

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

kaust-iit-collaboration-life-sciences-nanotechnology-research

KAUST collaboration to benefit life sciences and nanotechnology research

1 min read · Mon, Feb 3 2025

News Clip News

nanotechnology AI

KAUST and IIT unite to enhance research in life sciences, AI, robotics and nanotech, driving innovation through collaboration.

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Mhd. Emad Alolaby

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Mohammed A Shukri

Consultant (former), King Abdullah University of Science and Technology

Neural Networks AI Python (Programming Language) BLE communication

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Yang Xiao

Ph.D. Student, Statistics

spatio-temporal statistics bayesian inference Public Health Adaptive modeling forecasting Python (Programming Language)

Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python (Programming Language) TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python (Programming Language)

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Amal Alghamdi

HPC Python (Programming Language) data analysis finite element method uncertainty quantification

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab

communication engineering Data detection and decoding coding techniques for Terahertz Massive MIMO Systems Signal processing wireless communication MATLAB Python (Programming Language)

Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Mohammed A Shukri

Consultant (former), King Abdullah University of Science and Technology

Neural Networks AI Python (Programming Language) BLE communication

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Xiaotian Jin

Ph.D. Student, Statistics

Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization

Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.

Paolo Victor Redondo

Ph.D. Student, Statistics

extreme statistics Time Series Public Health brain signals Python (Programming Language) R (Programming Language)

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

Junshu Jiang

Ph.D. Student, Statistics

Quantitative finance Statistical Modelling extreme events numerical analysis qualitative research Linux Python (Programming Language)

Fernando Rodriguez Avellaneda

Ph.D. Student, Statistics

spatio-temporal modeling environmental research Spatial epidemiology Python (Programming Language) data science

Fernando Rodriguez Avellaneda's research focuses on Bayesian spatial and spatio-temporal modeling, with applications in environmental and epidemiological studies.

Hans Montcho

Ph.D. Student, Statistics

Bayesian computational statistics machine learning Bayesian computational technique cross validation point processes statistics Python (Programming Language)

Hans Montcho is a Ph.D. Ph.D. candidate In Statistics at the King Abdullah University of Science and Technology (KAUST).

Statistical Modeling of Financial Extremes and Volatility Dynamics

Junshu Jiang, Ph.D. Student, Statistics

Apr 6, 14:00 - 17:00

B2 R5220

Quantitative finance Statistical Modeling extreme events numerical analysis

This thesis provides comprehensive statistical tools for understanding and modeling extreme risks and volatility dynamics in financial markets.

Gaussian Random Fields on Metric Graphs

Gaussian random fields Metric graphs Statistical Modeling

Sep 11, 12:00 - 13:00

B9 B2 L2325

This talk presents a comprehensive mathematical and statistical theory, along with user-friendly software, for modeling data with Gaussian random fields on metric graphs by developing valid covariance functions based on network distance.

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

The two faces of correlation

Haavard Rue, Program Chair, Statistics

Sep 3, 12:00 - 13:00

KAUST

Statistical Modeling computing

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

1 min read · Sun, Aug 16 2020

Statistical Modeling Environmental Statistics

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 · Thu, Sep 3 2020

Statistical Modeling spatio-temporal statistics

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

Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization

Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.

Jordan Richards

Postdoctoral Research Fellow, Statistics

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational statistics INLA

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

functional data analysis Quantile spectral methods Functional Time Series forecasting Statistical Modeling Environmental 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

Statistics of extremes Environmental Statistics spatial statistics Statistical Modeling computational 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

Juan Pablo Muñoz Díaz

Ph.D. Student, Applied Mathematics and Computational Science

applied mathematics bioscience Dynamical Systems deep learning bifurcation theory

1 min read · Thu, May 25 2017

synthetic biology bioscience Computer science

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

Computer models combat malaria

1 min read · Sun, Apr 15 2018

bioscience drug discovery human health

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 · Sun, Jul 21 2019

brain science bioscience statistics

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

Simon Nik

Postdoctoral Research Fellow, Statistics

Statistics of extremes extreme-value theory stochastic partial differential equations discrete time series

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

spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

Apr 5, 15:00 - 17:00

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 · Thu, Dec 17 2015

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 · Sun, Nov 20 2016

Spatial extremes extreme-value theory Statistics of extremes

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 · Sun, Dec 18 2016

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

extreme-value theory Statistics of extremes copulas Bayesian Statistics risk assessment

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

spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

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

Statistics of extremes extreme-value theory spatio-temporal statistics data science machine learning copulas Environmental Statistics geostatistics applications to finance applications to neuroscience

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

spatial statistics extreme-value theory spatio-temporal statistics computational 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

extSTAT Research Group Time Series extreme-value theory

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

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

geospatial statistics spatio-temporal statistics High Performance Computing HPC multivariate statistics GPU Algorithms

May 14, 15:00 - 17:00

B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

Energy-Efficient and Sustainable Spatial Modeling Using GPU Computing

Sameh Abdulah, Senior Research Scientist, Applied Mathematics and Computational Science

Jan 29, 12:00 - 13:00

B9 L2 R2325

HPC

This talk highlights recent advances in energy-efficient and sustainable spatial modeling using GPU computing. It focuses on mixed-precision algorithms and scalable spatial statistical modeling that significantly reduce computational cost and power consumption while preserving scientific accuracy.

KAUST researchers define the emerging field of high-performance statistical computing (HPSC)

4 min read · Sun, Dec 14 2025

exascale GPU HPC mixed-precision computing Statistical computing

KAUST researchers outline a new discipline for statistical computing at supercomputing scale.

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

statistics spatio-temporal statistics GPU Computing HPC

Nov 12, 09:00 - 11:00

B5 L5 R5209

This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy.

Amal Alghamdi

HPC Python (Programming Language) data analysis finite element method uncertainty quantification

Saverio Pasqualoni

M.S. Student, Computer Science

collective communication HPC computer networking and systems

Saverio is a Master’s student pursuing graduate studies (M.S. and Ph.D.) in Computer Science at KAUST, working in the SANDS research group on high-performance computing and collective communications, with research interests that bridge systems-level computer science and large-scale simulation through a strong mathematical foundation.

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

1 min read · Fri, Nov 22 2024

News Clip News

HPC climate projections extreme computing computational simulations uncertainty quantification

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 · Mon, Sep 7 2020

Maximum Likelihood HPC Environmental Statistics

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

Xiran Zhang

Ph.D. Student, Statistics

statistics High Performance Computing HPC geospatial statistics spatio-temporal statistics

Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.

Esmail Abdul Fattah

Postdoctoral Research Fellow, 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

Racha Zayni

Visiting Researcher, Information Science Lab

machine learning Data Analytics Wireless Communications AI-based Localization Signal processing

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

Reem Arnaout

Intern, King Abdullah University of Science and Technology

wireless communication Signal processing Radio Frequency Localization Differential privacy Federated learning Network and Software Security

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

Serine Medjber

Visiting Student (former), King Abdullah University of Science and Technology

Signal processing embedded systems IoT

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

Mohammed Amine Labbas

Visiting Student (former), King Abdullah University of Science and Technology

IoT Signal processing wireless communications. embedded systems

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

Chengjie Zhao

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab

communication engineering Data detection and decoding coding techniques for Terahertz Massive MIMO Systems Signal processing wireless communication MATLAB Python (Programming Language)

Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab

Physically consistent modeling Near-field propagation analysis Signal processing mmWave systems Reconfigurable Intelligent Surfaces Sparse signal recovery 6G

Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

Visiting Scholar (former), King Abdullah University of Science and Technology

Signal processing Wireless Communications machine learning

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

Alaa Alameer Ahmad

Visiting Researcher (former), King Abdullah University of Science and Technology

Signal processing communication networks

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

M.S. (former), Electrical and Computer Engineering

Signal processing communication Network Design

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

Disease Nowcasting Using Integrated and Adaptive Statistical Models

Yang Xiao, Ph.D. Student, Statistics

Apr 28, 10:00 - 12:30

B5 R5220; Zoom Meeting 93296195795

Adaptive modeling Public Health bayesian inference

This thesis provides a comprehensive Bayesian nowcasting framework that addresses reporting delays, integrates complementary data sources, and improves real-time estimation of disease activity.

Yang Xiao

Ph.D. Student, Statistics

spatio-temporal statistics bayesian inference Public Health Adaptive modeling forecasting Python (Programming Language)

Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.

Bayesian Inference for Partially Observed Continuous-Time Processes

Amin Wu, Ph.D., Statistics

Mar 3, 10:00 - 12:00

B5 L5 R5220

McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.

Bayesian Modeling of Sub-Asymptotic Spatial Extremes

spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

Apr 5, 15:00 - 17:00

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

Daniele Durante, Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy

Nov 8, 15:00 - 16:00

B1 L4 R4102

bayesian inference

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 · Sun, Oct 6 2019

uncertainty quantification bayesian inference computational fluids mechanics

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

spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

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, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide 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

Dean, Computer, Electrical and Mathematical Sciences and Engineering

uncertainty quantification bayesian inference computational fluids mechanics combustion High Performance Computing

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.

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

1 min read · Sun, Jan 24 2016

FD-MIMO cognitive radio systems communications

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 · Tue, Jul 17 2018

FD-MIMO communications cognitive radio systems MOVPE laser solar hydrogen biophotonics bioimaging 3D printing

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

1 min read · Wed, Jan 2 2019

underwater sensor platform electrical engineering communications

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 · Wed, Apr 17 2019

communications electrical engineering sensors

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 · Thu, Dec 12 2019

communications electrical engineering IoT

Hovering airborne vehicles could connect smart sensors to the internet of things.

Internet of underground things to reveal buried vistas

1 min read · Sun, Feb 9 2020

fuel-resources communications electrical engineering

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

Angling for underwater WiFi

1 min read · Wed, Jun 10 2020

communications Computer science

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

Tareq Al-Naffouri

Professor, Electrical and Computer Engineering

Deeper defense against cyber attacks

1 min read · Sun, Nov 28 2021

cybersecurity machine learning Deep learning statistics

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.

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python (Programming Language) TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Amal Alghamdi

HPC Python (Programming Language) data analysis finite element method uncertainty quantification

Nizar Ajeeb

Researcher (former), King Abdullah University of Science and Technology

Programming language MATLAB data analysis Web Technologies

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

High-dimensional Q-learning for Dynamic Treatment Regimes

Prof. Rui Song, Department of Statistics, North Carolina State University

Mar 31, 12:00 - 13:00

B3 L5 R5220

HQ-learning statistical inference data analysis simulation

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 · Wed, Jun 24 2015

data analysis visuanimation statistics

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

1 min read · Sat, Nov 10 2018

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.

statistics data analysis

SRSI student success story at Ibdaa 2022

1 min read · Sun, Mar 20 2022

environmental applications data analysis computational methods

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

Bayesian computational statistics data analysis

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

Anass B. El-Yaagoubi

Postdoctoral Research Fellow, Statistics

Time Series neuroscience data analysis brain signals Generative Adversarial Networks topological ranking

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

neuroscience machine learning human health statistics

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.

Modeling Complex Dependence in Multivariate Time Series, with Applications to Brain Signals

May 5, 15:00 - 17:00

extreme statistics Biostatistics brain signals multivariate statistics Time Series

This thesis develops approaches to address issues and limitations associated with multivariate processes generated from unknown complex systems such as brain signals.

KAUST Ph.D. candidate Mara Sherlin Talento wins International Statistics Institute (ISI) Services Award

3 min read · Tue, Nov 25 2025

KAUST Ph.D. candidate Mara Sherlin Talento has received the International Statistics Institute (ISI) Services Award in recognition of her leadership, global outreach, and contributions to the development of young statisticians.

Biostatistics

Meet KAUST new student: Daniel Osorio Barreto

1 min read · Wed, Sep 1 2021

Biostatistics macroeconomics

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

Biostatistics

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

Biostatistics Spectral Analysis Neuroimaging

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

Bayesian computational statistics Survival analysis Biostatistics

Farah Abdulrazak Gomawi

Ph.D. Student, Statistics

Time Series Neuroimaging Electroencephalograms Functional Near-Infrared Spectroscopy Spectral Analysis Biostatistics

Farah Abdulrazak Gomawi is a Ph. D. student in Statistics at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Hernando Ombao. Education and Early Career Farah Abdulrazak Gomawi received her Bachelor of Science in Applied Mathematics from the University of California, Los Angeles (UCLA) in the United States, with the KAUST Gifted Student Program (KGSP) scholarship in 2023. Prior to this, she was a visiting student in the Biostatistics group in the summer of 2022. Research Interest Farah's research interests include time series analysis, machine

Daniel Osorio Barreto

Ph.D. Student, Statistics

Biostatistics behavioral economics macroeconomics

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.

Efficient Numerical Methods for Scalable Bayesian Inference

Lisa Gaedke-Merzhäuser, Postdoctoral Research Fellow, Statistics

Apr 16, 12:00 - 13:00

B9 R2325

numerical methods Efficient Bayesian Inversion Automated and scalable algorithms

This talk will introduce and discuss the computational building blocks of Integrated Nested Laplace Approximations (INLA) and how these are implemented in software libraries such as R-INLA or DALIA.

Extrapolated Linear Multistep Methods

Lajos Lóczi, Research Scientist, Applied Mathematics and Computational Science

Mar 12, 12:00 - 13:00

B9 L2 R2325

ODEs PDEs numerical methods linear multistep methods LMMs

In this talk, we will discuss how linear multistep methods and classical extrapolation can be combined to obtain new classes of efficient time-integration methods.

Lajos Lóczi

Research Scientist, Applied Mathematics and Computational Science

numerical methods PDEs ODEs stability analysis numerical discretization qualitative theory numerical analysis

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

Salim Ksous

M.S. Student, Stochastic Numerics Research Group

optimal control numerical methods

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

Alfio Quarteroni, Emeritus Professor, Politecnico di Milano and EPFL

Jan 25, 14:00 - 15:00

B9, L2, R2322

Computational mathematics numerical methods Scientific Machine Learning scientific computing applied mathematics

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

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

Raul Tempone, Professor, Applied Mathematics and Computational Science

May 22, 12:00 - May 28, 12:00

B9 H1 R2322

numerical methods stochastic differential equations statistics

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 · Thu, Jul 24 2014

uncertainty quantification stochastic differential equations numerical methods

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 · Sun, Jan 10 2016

statistics numerical methods stochastic differential equations

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.

Disease Nowcasting Using Integrated and Adaptive Statistical Models

Yang Xiao, Ph.D. Student, Statistics

Apr 28, 10:00 - 12:30

B5 R5220; Zoom Meeting 93296195795

Adaptive modeling Public Health bayesian inference

This thesis provides a comprehensive Bayesian nowcasting framework that addresses reporting delays, integrates complementary data sources, and improves real-time estimation of disease activity.

Yang Xiao

Ph.D. Student, Statistics

spatio-temporal statistics bayesian inference Public Health Adaptive modeling forecasting Python (Programming Language)

Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

Apr 30, 13:00 - 16:00

B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Geospatial Data Science for Public Health Surveillance

Geospatial Data Public Health Public health surveillance geospatial statistics statistical methods

Mar 9, 11:15 - 12:45

B4/5 L0 A0215; Zoom Meeting 94713495879

This talk presents an overview of our research on innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and how this work has directly informed strategic policy to reduce disease burden.

Geospatial Data Science for Public Health Surveillance

statistical methods geospatial data analysis health surveillance Public Health spatio-temporal data analysis

Nov 20, 12:00 - 13:00

B9 L2 R2325

This talk will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance, highlighting challenges related to data biases and availability.

Geospatial Data Science for Public Health Surveillance - Seminar

Paula Moraga, Lecturer, Department of Mathematical Sciences, University of Bath, UK

Feb 5, 12:00 - 13:00

B9 L2 H2

Geospatial Data Public Health

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.

Xiang Chen

Ph.D. Student, Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Paolo Victor Redondo

Ph.D. Student, Statistics

extreme statistics Time Series Public Health brain signals Python (Programming Language) R (Programming Language)

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

Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

Matthew Pratola, Assistant Professor of Statistics, The Ohio State University

Nov 24, 15:00 - 16:00

B1 L2

nonparametric statistics Bayesian Statistics

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

Cristian Chiuchiolo, Ph.D. Student, Electrical and Computer Engineering

Jun 6, 15:00 - 17:00

B3 L5 R5209

Bayesian computational statistics Bayesian Statistics

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 · Tue, Jun 25 2019

Statistics of extremes Bayesian Statistics

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 · Wed, Nov 27 2019

Bayesian Statistics Marko Chain Theory

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

extreme-value theory Statistics of extremes copulas Bayesian Statistics risk assessment

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

Bayesian Statistics Deep learning Gaussian processes

KAUST Associate Professor of Statistics Maurizio Filippone’s primary research focus is Bayesian statistics to enable 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

spatio-temporal statistics Bayesian 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

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

3 min read · Sun, Apr 20 2025

climate data stochastic computational science climate change

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

Low Rank Everywhere (Almost)

David Keyes, Professor, Applied Mathematics and Computational Science

Haavard Rue, Program Chair, Statistics

Sep 3, 12:00 - 13:00

KAUST

Statistical Modeling computing

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 · Sat, Jul 11 2015

applied mathematics computational science computing

A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Giving power to demand-driven electricity

1 min read · Sat, Feb 4 2017

applied mathematics computational science computing Computer science

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 · Sat, Mar 4 2017

statistics computing applied mathematics

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 · Mon, Jan 1 2018

computing statistics engineering

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 · Sun, Mar 18 2018

statistics computing healthcare

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 · Sun, May 20 2018

computing earth science statistics Environmental Statistics

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

Emmanuel Ambriz

Postdoctoral Research Fellow, Statistics

multivariate analysis functional data analysis nonparametric statistics

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

Mar 12, 12:00 - 13:00

KAUST

functional data analysis spatio-temporal statistics

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

Zhuo Qu, Ph.D. Student, Statistics

Nov 7, 11:00 - 13:00

B3 L5 R5220

functional data analysis multivariate functional data statistics

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 · Mon, Nov 4 2019

Stochastic processes Gaussian processes functional data analysis

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

Stochastic processes Gaussian processes functional data analysis

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

functional data analysis multivariate analysis spatial statistics quantile regression non-parametric methods

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

functional data analysis Quantile spectral methods Functional Time Series forecasting Statistical Modeling Environmental 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.

Mapping risk on networks: A scalable model for spatial point processes earns international recognition

2 min read · Mon, Jul 28 2025

spatial statistics Gaussian processes R-INLA

KAUST Ph.D. student Damilya Saduakhas wins Best Paper Award for a new model mapping accident risk on complex road networks.

A new class of random field models for data on networks

applied statistics machine learning Gaussian processes

Oct 3, 12:00 - 13:00

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 · Mon, Nov 4 2019

Stochastic processes Gaussian processes functional data analysis

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 · Sun, Jul 5 2020

Gaussian processes spatio-temporal statistics

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

Bayesian Statistics Deep learning Gaussian processes

KAUST Associate Professor of Statistics Maurizio Filippone’s primary research focus is Bayesian statistics to enable 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

Stochastic processes Gaussian processes functional data analysis

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.

Amin Wu

Ph.D., Statistics

spatio-temporal statistics Gaussian processes

Simulation of Neuronal Signal Processing - 2020-03-05

Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

Postdoctoral Research Fellow, Statistics

Time Series neuroscience data analysis brain signals Generative Adversarial Networks topological ranking

Ph.D. Student, Photonics Laboratory

sensors Photonics and optoelectronics

Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.

Winds and waves winding down

1 min read · Sat, Jun 17 2017

earth science and engineering climatology Red Sea

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 · Sun, Jul 30 2017

earth science and engineering climatology Red Sea modeling

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

1 min read · Sun, Jul 22 2018

earth science and engineering marine science oceanography Red Sea

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 · Thu, May 2 2019

earth science and engineering marine science ocean circulation Red Sea

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 · Sun, Jun 23 2019

oceanography Red Sea earth science and engineering

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

1 min read · Thu, Jun 13 2019

Red Sea environment marine science earth science and engineering

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 · Thu, Apr 8 2021

big data Red Sea climate science statistics

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.

Ansar Khangeldin

M.S. Student

Computer Vision Reinforcement Learning Multi-agent systems autonomous systems

Ms/PhD Electrical and Computer Engineering student. Have a background in Applied Mathematics, Control Theory, Machine Learning Research and Development, Multi-Agent Reinforcement Learning. Main research interests are application and enhancement of CV methods in autonomous systems (AVs, Robotics, UAVs).

Echo Ziyi Yang

Ph.D. Student

Autonomous Agent machine learning Reinforcement Learning

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering

Wireless Underground Sensor Networks LoRAWAN Space-Air-Ground-Underground Integrated Networks Wireless Energy Transfer Reinforcement Learning

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

UAV adaptive control Reinforcement Learning Aerial Robotics

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

AI Deep learning Computer Vision Reinforcement Learning

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

Oct 19, 16:00 - 17:00

B9 L2 H2

Reinforcement Learning Digital Behavioral Health

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

Oct 20, 15:00 - 16:00

B9 L2 H2

Reinforcement Learning

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.

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python (Programming Language) TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

Kelvin J. R. Almeida-Sousa

Postdoctoral Research Fellow, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Kelvin J. R. Almeida-Sousa is a Brazilian mathematician with contributions to deterministic and stochastic partial differential equations, numerical analysis, mathematical statistics, and spectral theory.

David Bolin

Professor, Statistics

mathematical statistics random fields stochastic partial differential equations computational statistics stochastic processes

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.

Alexandre de Bustamante Simas

Senior Research Scientist, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Alexandre Simas is an expert on stochastic processes and spatial statistics, with a focus on stochastic partial differential equations and random fields on metric graphs.

Nebras Filimban

Ph.D. Candidate, Computer, Electrical and Mathematical Sciences and Engineering

RFICs 6G MIMO beamforming systems

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab

Physically consistent modeling Near-field propagation analysis Signal processing mmWave systems Reconfigurable Intelligent Surfaces Sparse signal recovery 6G

Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

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

extreme-value theory Statistics of extremes copulas Bayesian Statistics risk assessment

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

Statistics of extremes extreme-value theory spatio-temporal statistics data science machine learning copulas Environmental Statistics geostatistics applications to finance applications to neuroscience

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

copulas Multivariate extremes nonparametric statistics Spatial data

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

copulas Statistics of extremes nonparametrics

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

SPDE-Based Geostatistical and Point Process Models for Environmental and Urban Network Applications

Damilya Saduakhas, Ph.D. Student, Statistics

Apr 15, 13:00 - 15:00

B5 R5209; Zoom Meeting 96585312249

SPDEs geostatistics oceanography monitoring multivariate spatial processes environmental applications

This thesis develops statistical models based on stochastic partial differential equations (SPDEs) for geostatistical and point process data, with applications to oceanographic monitoring, traffic safety, and urban air quality.

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read · Wed, Jul 23 2014

spatio-temporal statistics environmental applications computational methods

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 · Sun, Aug 13 2017

spatio-temporal statistics environmental applications computational methods

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 Associationâs Section on Statistics and the Environment (ENVR).

SRSI student success story at Ibdaa 2022

1 min read · Sun, Mar 20 2022

environmental applications data analysis computational methods

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

spatio-temporal statistics environmental applications computational 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

geospatial statistics environmental applications multivariate spatial processes

Modeling Complex Dependence in Multivariate Time Series, with Applications to Brain Signals

May 5, 15:00 - 17:00

extreme statistics Biostatistics brain signals multivariate statistics Time Series

This thesis develops approaches to address issues and limitations associated with multivariate processes generated from unknown complex systems such as brain signals.

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

Daniela Cisneros, Ph.D. Student, Statistics

Sep 11, 16:00 - 17:00

B3 L5 R5220

extreme statistics Applied Machine Learning geospatial statistics

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 · Sun, Dec 13 2020

statistics extreme statistics

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 · Sun, Apr 25 2021

extreme statistics big data statistics

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 · Thu, Jan 20 2022

extreme statistics modeling big data statistics

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. Student, Statistics

extreme statistics Time Series Public Health brain signals Python (Programming Language) R (Programming Language)

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

Xia Dexiao

Research Staff, Computer, Electrical and Mathematical Sciences and Engineering

Electromagnetic metamaterials ISAC Phased arrays design

Dr. Dexiao Xia is a Postdoctor in the Computer, Electrical, and Mathematical Sciences and Engineering Division at KAUST. His research focuses on phased-array, integrated sensing and communication (ISAC), and AI-driven adaptive system.

Changhao He

Next generation wireless communications ISAC UAV-enabled wireless communications machine learning for wireless communication

Yousef Awad

ISAC Unmanned aerial vehicles UAVs reconfigurable intelligent surfaces applications

Bumin Kagan Yildirim

artificial intelligence for wireless communication ISAC distributed multimodal ISAC Semantic Communication

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering

FDA-MIMO Radar Talget Detection ISAC haps remote sensing

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

Non-Terrestrial Networks haps ISAC

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

Time Series Clustering: Pattern Recognition, Forecasting, and Amortized Inference

Ángel López Oriona, Postdoctoral Research Fellow, Statistics

May 14, 12:00 - 13:00

B9 R2325

Time Series Pattern Recognition forecasting statistical inference

This talk presents innovative time series clustering techniques, highlighting a quantile-based approach for analyzing locally stationary data, a predictive framework for enhanced forecasting, and the use of amortized inference to overcome traditional algorithmic limitations.

Fully Decentralized Inference for Spatial Data Using Low-Rank Models

Jianwei Shi, Postdoctoral Research Fellow, Statistics

May 7, 12:00 - 13:00

B9 R2325

statistical inference decentralized systems Geospatial Data geospatial statistics optimization

This talk introduces a novel, fully decentralized optimization framework to enable scalable parameter inference in large spatial low-rank models, supported by both theoretical guarantees and empirical validation.

High-dimensional Q-learning for Dynamic Treatment Regimes

Prof. Rui Song, Department of Statistics, North Carolina State University

Mar 31, 12:00 - 13:00

B3 L5 R5220

HQ-learning statistical inference data analysis simulation

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.

Transparent and Robust Causal Inference in Data Science

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

Nov 30, 16:30 - 17:30

KAUST

statistical inference data science

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.

Kelvin J. R. Almeida-Sousa

Postdoctoral Research Fellow, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Kelvin J. R. Almeida-Sousa is a Brazilian mathematician with contributions to deterministic and stochastic partial differential equations, numerical analysis, mathematical statistics, and spectral theory.

Alexandre de Bustamante Simas

Senior Research Scientist, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Alexandre Simas is an expert on stochastic processes and spatial statistics, with a focus on stochastic partial differential equations and random fields on metric graphs.

Shyam Mohan Subbiah Pillai

Research Consultant, Stochastic Numerics Research Group

numerical analysis uncertainty quantification stochastic differential equations

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

Raul Tempone, Professor, Applied Mathematics and Computational Science

May 22, 12:00 - May 28, 12:00

B9 H1 R2322

numerical methods stochastic differential equations statistics

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

Oct 13, 15:30 - 16:30

KAUST

parameter estimation stochastic differential equations stochastic

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 · Thu, Jul 24 2014

uncertainty quantification stochastic differential equations numerical methods

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 · Sun, Jan 10 2016

statistics numerical methods stochastic differential equations

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, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide 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.

Racha Zayni

Visiting Researcher, Information Science Lab

machine learning Data Analytics Wireless Communications AI-based Localization Signal processing

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

Chaabane Mankai

Wireless Communications optimization Performance analysis network security

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

Saleh AlJazzar

Visiting Scholar (former), King Abdullah University of Science and Technology

Signal processing Wireless Communications machine learning

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

Connecting the Unconnected at the KAUST 6G Summit

1 min read · Sun, Nov 1 2020

6G Wireless Communications communication technology

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 · Tue, Dec 1 2020

Wireless Communications IEEE award

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 · Sun, Sep 26 2021

wireless networks 6G Wireless Communications global connectivity

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.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read · Wed, Jan 21 2026

optimization machine learning communication efficiency artificial intelligence big data algorithm

The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Helping computers to see who we really are

1 min read · Sat, Oct 8 2016

algorithm machine learning algorithm

Algorithms that train computers to automatically detect human activity in videos can improve online searches and real-world surveillance systems.

Changing how we predict coral bleaching

1 min read · Tue, Jun 18 2019

algorithm coral bleaching climate science earth science and engineering

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

Teamwork pays off at startup Insyab

1 min read · Mon, Jul 8 2019

robot drones wireless connectivity algorithm Energy-efficient wireless multihop networks Cross-layer protocol design

Insyab, a technology startup specializing in smart solutions allowing robots and drones to collaborate on the execution of common tasks, resulted from three years of its founders' dedicated research at KAUST.

Training an AI eye on the Moon

1 min read · Mon, Jul 12 2021

machine learning algorithm modeling Computer science

Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Sep 4, 12:00 - 13:00

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

Nov 21, 12:00 - 13:00

B9 L2 H1 R2322

Fractional differential equations Partial Differential Equations Estimation problems Simulations algorithms real applications

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.

Machine learning models gather momentum

1 min read · Sun, Nov 24 2019

algorithms machine learning Computer science

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

Xiaotian Jin

Ph.D. Student, Statistics

Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization

Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.

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.

Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

Yiping Hong, Postdoctoral Research Fellow, Statistics

Oct 22, 12:00 - 13:00

KAUST

mathematics applied statistics Social Network Analysis

Solvers for Coupled Problems in Porous Media Science

Arne Naegel

Sep 13, 12:00 - 13:00

B9 L2 R2322

applied statistics

No abstract is available.

A new class of random field models for data on networks

applied statistics machine learning Gaussian processes

Oct 3, 12:00 - 13:00

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

mathematical modelling visualization computational predictions spatio-temporal data analysis applied 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

M.S., Statistics

machine learning Deep learning applied statistics

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

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read · Wed, Jul 23 2014

spatio-temporal statistics environmental applications computational methods

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 · Sun, Aug 6 2017

big data artificial intelligence computational methods health protein structures biology

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 · Sun, Aug 13 2017

spatio-temporal statistics environmental applications computational methods

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 Associationâs Section on Statistics and the Environment (ENVR).

Using Statistical Models to Solve Environmental Problems

1 min read · Wed, Jan 15 2020

spatio-temporal statistics computational methods

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 · Sun, Mar 20 2022

environmental applications data analysis computational methods

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.

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

Apr 30, 13:00 - 16:00

B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

Professor Marc Genton named Distinguished Professor of Statistics at KAUST

1 min read · Wed, Oct 26 2016

computational predictions mathematical modelling visualization

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 · Wed, Feb 28 2018

Awards Spotlight

Mathematical modeling visualization computational predictions

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 · Sun, Sep 15 2019

statistics visualization computational predictions

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

mathematical modelling visualization computational predictions spatio-temporal data analysis applied 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.

Tao Ni

Assistant Professor, Computer Science

Cyber Security resilient cyber-physical

Professor Tao Ni (Tony) is focused on building secure, reliable and energy-efficient cyber-physical systems to enable trustworthy computing and confidential sensing in critical infrastructure. His approach combines hardware-software co-design with advanced AI technologies.

Mehdi Zakroum

Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Web Security Trustworthy AI

Ahmar Husain

Visiting Student, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Malware Analysis LLM

Hassan Hadi

Research Specialist, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security cyber threats SOC UAV Security IDPS

Research Specialist on Cyber Security

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

How extreme weather exacerbates air pollution

1 min read · Tue, Sep 4 2018

statistics extreme weather air pollution xstat research highlights

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

Reining in computational complexity

1 min read · Tue, Oct 2 2018

statistics extreme weather

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 · Tue, Jun 25 2019

extreme weather spatio-temporal statistics Statistics of extremes

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 · Sun, Sep 12 2021

extreme weather climate change statistics

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 · Thu, Apr 28 2022

extreme weather modeling modeling climate science statistics

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.

Fully Decentralized Inference for Spatial Data Using Low-Rank Models

Jianwei Shi, Postdoctoral Research Fellow, Statistics

May 7, 12:00 - 13:00

B9 R2325

statistical inference decentralized systems Geospatial Data geospatial statistics optimization

This talk introduces a novel, fully decentralized optimization framework to enable scalable parameter inference in large spatial low-rank models, supported by both theoretical guarantees and empirical validation.

Geospatial Data Science for Public Health Surveillance

Geospatial Data Public Health Public health surveillance geospatial statistics statistical methods

Mar 9, 11:15 - 12:45

B4/5 L0 A0215; Zoom Meeting 94713495879

This talk presents an overview of our research on innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and how this work has directly informed strategic policy to reduce disease burden.

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

machine learning Geospatial Data GPU Computing

May 8, 12:00 - 13:00

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

Feb 5, 12:00 - 13:00

B9 L2 H2

Geospatial Data Public Health

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

spatial statistics Point patterns Functional data R project INLA spatio-temporal statistics Geospatial Data

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

Babar Khan

Ph.D. (former), Bioscience

biotechnology healthcare

Ph.D. degree in King Abdullah University of Science and Technology (KAUST)

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

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

Smart healthcare uses stats to spot a stumble

1 min read · Sat, Apr 15 2017

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

statistics healthcare

Zooming in on brain function

1 min read · Sun, Mar 18 2018

statistics computing healthcare

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

Solving real-world problems

1 min read · Wed, Sep 19 2018

statistics Bayesian computational statistics healthcare ecology

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.

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

geospatial statistics spatio-temporal statistics High Performance Computing HPC multivariate statistics GPU Algorithms

May 14, 15:00 - 17:00

B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

Pulling rank on spatial statistics

1 min read · Sun, Feb 18 2018

High Performance Computing statistics

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

KAUST Ph.D. student wins best paper award from American Statistical Association

1 min read · Thu, Apr 25 2019

Awards News Spotlight

Statistical computing High Performance Computing

KAUST Ph.D. statistics student Jian Cao was recently selected as a best paper award winner by the American Statistical Association (ASA) for his paper entitled "Computing High-Dimensional Normal and Student-t Probabilities with Tile-Low-Rank Quasi-Monte Carlo and Block Reordering." Cao's paper was chosen in an ASA student paper competition under the section on Statistical Computing.

Xiran Zhang

Ph.D. Student, Statistics

statistics High Performance Computing HPC geospatial statistics spatio-temporal statistics

Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.

Omar Knio

Dean, Computer, Electrical and Mathematical Sciences and Engineering

uncertainty quantification bayesian inference computational fluids mechanics combustion High Performance Computing

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.

The Role of Humans in Scientific Discovery in the Age of LLMs — Beyond Asking: Turning LLMs into Research Collaborators

Sir Bashir M. Al-Hashimi, Vice President, Research & Innovation, King’s College London (KCL); Distinguished Professor, Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King’s College London (KCL)

Apr 8, 12:00 - 14:15

B9 R2325

AI artificial intelligence LLM scientific research scientific knowledge Assistive Technology

Rather than offering definitive conclusions, this talk seeks to stimulate dialogue, question assumptions, and inspire new forms of collective thinking about the future of scientific research and doctoral training in an AI-driven world.

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Ahmar Husain

Visiting Student, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Malware Analysis LLM

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

Sep 4, 12:00 - 13:00

B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Muhammad Samiullah

Intern, Cyber Security and Resilience Technology

cybersecurity ML FPGA LLM

Cyber Security, FPGA security, AI/ML security

Emmanuel Ambriz

Postdoctoral Research Fellow, Statistics

multivariate analysis functional data analysis nonparametric statistics

Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling

Matthew Pratola, Assistant Professor of Statistics, The Ohio State University

Nov 24, 15:00 - 16:00

B1 L2

nonparametric statistics Bayesian Statistics

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

Time Series Analysis robust statistics nonparametric statistics

Zhuo Qu

Ph.D. Student, Statistics

statistics Functional data robust statistics nonparametric statistics spatio-temporal 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

copulas Multivariate extremes nonparametric statistics Spatial data

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

Katerina Nik

Assistant Professor, Applied Mathematics and Computational Science

Partial Differential Equations free boundary problems calculus of variations Mathematical Biology Microelectromechanical fluid dynamics mean curvature flow

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

Apr 18, 12:00 - 13:00

B9 L2 H1

Fractional differential equation Partial Differential Equations

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

Nov 21, 12:00 - 13:00

B9 L2 H1 R2322

Fractional differential equations Partial Differential Equations Estimation problems Simulations algorithms real applications

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.

Kelvin J. R. Almeida-Sousa

Postdoctoral Research Fellow, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Kelvin J. R. Almeida-Sousa is a Brazilian mathematician with contributions to deterministic and stochastic partial differential equations, numerical analysis, mathematical statistics, and spectral theory.

Alexandre de Bustamante Simas

Senior Research Scientist, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory

Alexandre Simas is an expert on stochastic processes and spatial statistics, with a focus on stochastic partial differential equations and random fields on metric graphs.

Statistics of Extremes

Raphaël Huser, Associate Professor, Statistics

Sep 26, 12:00 - 13:00

B9 L2 H1 R2322

extreme environmental events probabilities risks univariate extremes Spatial extremes

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 · Thu, Dec 17 2015

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 · Sun, Nov 20 2016

Spatial extremes extreme-value theory Statistics of extremes

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 · Sun, Dec 18 2016

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 · Mon, Jan 15 2018

statistics spatial statistics Spatial extremes

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.

Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil

spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions

Apr 30, 13:00 - 16:00

B5 R5209

This dissertation develops integrated spatio-temporal forecasting approaches that combine deep learning, climate data, spatial dependencies, and human mobility to improve dengue prediction and support early-warning systems in Brazil.

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

1 min read · Tue, Nov 19 2019

spatial statistics spatio-temporal modeling

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.

Xiang Chen

Ph.D. Student, Statistics

spatio-temporal modeling infectious disease geospatial statistics Spatial epidemiology Public Health Time Series Analysis deep learning explainable AI

Xiang Chen's research focuses on spatio-temporal modeling of climate-sensitive infectious diseases, integrating large-scale health, climate, and human mobility data to improve epidemiological forecasting and support public health decision-making.

Pratik Nag

Ph.D. Student, Statistics

spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational 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

Fernando Rodriguez Avellaneda

Ph.D. Student, Statistics

spatio-temporal modeling environmental research Spatial epidemiology Python (Programming Language) data science

Fernando Rodriguez Avellaneda's research focuses on Bayesian spatial and spatio-temporal modeling, with applications in environmental and epidemiological studies.

Brain waves in boxes

1 min read · Sat, Oct 3 2015

biomedicine statistics bioscience brain science

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

Connecting the dots to unlock brain structure

FD-MIMO communications cognitive radio systems MOVPE laser solar hydrogen biophotonics bioimaging 3D printing

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 · Sun, Aug 4 2019

communication networks wireless networks energy harvesting cognitive radio systems

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

FD-MIMO communications cognitive radio systems modulation schemes optical wireless communication systems

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.

A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis

Xin Gao, Professor, Computer Science

Apr 8, 12:00 - 13:00

KAUST

COVID-19

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 · Tue, Jun 9 2020

artificial intelligence COVID-19

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 · Wed, Jul 22 2020

Spectral Analysis numerical analysis COVID-19 research

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 · Tue, Jul 28 2020

COVID-19 artificial intelligence

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).

Getting a visual on complex data

1 min read · Thu, May 18 2017

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

applied mathematics data

Dataset size counts for better predictions

1 min read · Sun, Oct 8 2017

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

statistics data

Making collective sense of brainwaves

1 min read · Wed, Oct 31 2018

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

neuroscience data

Tweaking tools to track tweets over time

1 min read · Sun, Feb 17 2019

Social media data machine learning artificial intelligence

Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Dalal E. Aldossary

M.S. Student, Computer Science

machine learning Electroencephalograms Computer Vision Computational Neuroscience

Dalal Aldossary is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Hernando Ombao

Professor, Statistics

Time Series Spectral Analysis Wavelets Longitudinal Models Mixed Effects Models Functional Magnetic Resonance Imaging Electroencephalograms

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

Farah Abdulrazak Gomawi

Ph.D. Student, Statistics

Time Series Neuroimaging Electroencephalograms Functional Near-Infrared Spectroscopy Spectral Analysis Biostatistics

Farah Abdulrazak Gomawi is a Ph. D. student in Statistics at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Hernando Ombao. Education and Early Career Farah Abdulrazak Gomawi received her Bachelor of Science in Applied Mathematics from the University of California, Los Angeles (UCLA) in the United States, with the KAUST Gifted Student Program (KGSP) scholarship in 2023. Prior to this, she was a visiting student in the Biostatistics group in the summer of 2022. Research Interest Farah's research interests include time series analysis, machine

The KAUST 2023 Workshop on Statistics

Nov 6, 09:00 - Nov 9, 17:00

B18 R4203

statistics data science modeling big data sustainability health environment

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

Simple statistics can be good enough

1 min read · Tue, Apr 30 2019

Computer science statistics environment

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

Remote sensing of toxic algal blooms

1 min read · Thu, Jun 13 2019

Red Sea environment marine science earth science and engineering

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

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

1 min read · Thu, May 26 2022

award statistics environment

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.

Yang Xiao

Ph.D. Student, Statistics

spatio-temporal statistics bayesian inference Public Health Adaptive modeling forecasting Python (Programming Language)

Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.

Time Series Clustering: Pattern Recognition, Forecasting, and Amortized Inference

Ángel López Oriona, Postdoctoral Research Fellow, Statistics

May 14, 12:00 - 13:00

B9 R2325

Time Series Pattern Recognition forecasting statistical inference

This talk presents innovative time series clustering techniques, highlighting a quantile-based approach for analyzing locally stationary data, a predictive framework for enhanced forecasting, and the use of amortized inference to overcome traditional algorithmic limitations.

Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

Carolina D. Euan Campos, Postdoctoral Research Fellow, Statistics

Nov 28, 12:00 - 13:00

B9 H2 R2325

solar energy solar irradiance analysis nonlinearity TAR model statistical analysis forecasting

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

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics

functional data analysis Quantile spectral methods Functional Time Series forecasting Statistical Modeling Environmental 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.

Juan Pablo Bernal Tamayo

Ph.D., Applied Mathematics and Computational Science

PDEs genomics single cell biology

Exploring protein form to find function

1 min read · Sat, Jun 25 2016

Computer science genomics proteins

Existing systems for predicting protein structure are outperformed by a newly developed method.

Mining Red Sea bacteria for industrial potential

1 min read · Thu, Oct 4 2018

marine science bioprospecting microbiology genomics

Genome sequencing of two Red Sea bacteria highlights their potential as industrial workhorses.

Jesper Tegnér

Professor, Bioscience

living systems machine learning machine intelligence systems medicine genomics artificial intelligence brain-inspired computation Systems neuroscience algorithmic information theory Dynamical Systems spatial transcriptomics single cell biology

Jasper Tegnér 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.

Nikita Kondratyev

Research Specialist, Electrical and Computer Engineering

Nonlinear Optics integrated photonics Phased Arrays Numerical Modeling

Mikhail Mishevskii

Nonlinear Optics integrated photonics ultrafast fiber lasers

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aleksandr Movsisian

light–matter interaction integrated photonics Quantum optics quantum metrology optomechanics

Aleksandr received his bachelor’s degree in 2023 in Physics from Moscow State University (MSU, ranked first in Russia) and completed a master’s degree in Physics with honors at the same institution. He has over two years of research experience in quantum optics, optomechanics, and quantum measurement theory as part of the LIGO group at MSU. His work has focused on squeezed states of light, measurement topologies, and methods to overcome the Standard Quantum Limit, resulting in two first-author publications in JOSA B and Optics Letters. Aleksandr also gained hands-on experimental experience at

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering

Nonlinear Optics integrated photonics ultrafast fiber lasers Quantum nanomaterials

Reem Alzahrani

Ph.D. Student

Deep learning nlp Computer Vision Large Language Models vision language models Multi-Modal Large Language Models

Reem Alzahrani is an MS/PhD student at KAUST, working on Large Language Models and Vision-Language Models. Her research focuses on identifying and addressing the limitations of AI systems to improve their robustness and reliability.

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Zakaria El Kassimi

machine learning deep learning Large Language Models

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Zaid Alyafeai

Postdoctoral Research Fellow

Arabic NLP Large Language Models Cultural Understanding

Farah Souayfane

Research Scientist, Omar Knio Research Group

renewable energy energy systems stochastic optimization Thermal gradient Mathematical modeling energy efficiency in buildings

Farah Souayfane is a research scientist specializing in resilient renewable energy systems and energy-efficient building solutions for extreme desert climates.

C1+alpha Regularity for the Fractional p-Laplacian

David De Jesus, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Feb 19, 12:00 - 13:00

B9 L2 R2325

Mathematical modeling

In this talk, we will discuss a recent result obtained in collaboration with Davide Giovagnoli and Luis Silvestre, where we solve a standing conjecture asserting that, as in the local setting, solutions of the homogeneous equation are still Hölder differentiable.

Ali O Balubaid

Researcher, King Abdullah University of Science and Technology

living systems Cellular Networks Tissue Engineering bioscience Mathematical modeling

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

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read · Wed, Feb 28 2018

Awards Spotlight

Mathematical modeling visualization computational predictions

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

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

geospatial statistics spatio-temporal statistics High Performance Computing HPC multivariate statistics GPU Algorithms

May 14, 15:00 - 17:00

B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

Modeling Complex Dependence in Multivariate Time Series, with Applications to Brain Signals

May 5, 15:00 - 17:00

extreme statistics Biostatistics brain signals multivariate statistics Time Series

This thesis develops approaches to address issues and limitations associated with multivariate processes generated from unknown complex systems such as brain signals.

Data-driven Anomaly Detection in Industrial Processes

Fouzi Harrou, Senior Research Scientist, Statistics

Feb 12, 12:00 - 13:00

B9 L2 R2325

anomaly detection multivariate statistics artificial intelligence AI

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

Maicon J. Karling

Postdoctoral Research Fellow, Statistics

goodness-of-fit tests multivariate statistics skewed distributions Time Series

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.

Extrapolated Linear Multistep Methods

Lajos Lóczi, Research Scientist, Applied Mathematics and Computational Science

Mar 12, 12:00 - 13:00

B9 L2 R2325

ODEs PDEs numerical methods linear multistep methods LMMs

In this talk, we will discuss how linear multistep methods and classical extrapolation can be combined to obtain new classes of efficient time-integration methods.

Lajos Lóczi

Research Scientist, Applied Mathematics and Computational Science

numerical methods PDEs ODEs stability analysis numerical discretization qualitative theory numerical analysis

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python (Programming Language) TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Juan Pablo Bernal Tamayo

Ph.D., Applied Mathematics and Computational Science

PDEs genomics single cell biology

Event Status: Cancelled | Remote Sensing and Agroinformatics Insights in Saudi Arabia Using Machine Learning

Ting Li, Postdoctoral Research Fellow, Environmental Science and Engineering

Mar 5, 12:00 - 13:00

B9 L2 R2325

remote sensing machine learning sustainable agricultural agricultural productivity

This talk explores how machine learning and high-resolution satellite remote sensing are being used to transform vast amounts of raw data into actionable agroinformatics at a national scale, providing the precision needed to manage these vital resources sustainably.

Ting Li

Postdoctoral Research Fellow, Environmental Science and Engineering

remote sensing machine learning precision agriculture agriculture environmental modeling crop health agricultural productivity sustainable agricultural

Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Gleb Ludnikov

Free-space optical communications remote sensing optical imaging Terahertz communications

To strive and explore, to uncover and never give in.

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering

FDA-MIMO Radar Talget Detection ISAC haps remote sensing

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

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

Alfio Quarteroni, Emeritus Professor, Politecnico di Milano and EPFL

Jan 25, 14:00 - 15:00

B9, L2, R2322

Computational mathematics numerical methods Scientific Machine Learning scientific computing applied mathematics

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

solid mechanics biomechanics numerical analysis scientific computing

Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

Deep Learning and Scientific Computing

Jinchao Xu, Professor, Applied Mathematics and Computational Science

Sep 27, 12:00 - 13:00

B9 L2 R2322

Deep learning scientific computing

In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Xiaotian Jin

Ph.D. Student, Statistics

Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization

Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.

Zinan Xiao

Ph.D. Student, Photonics Laboratory

sensors Photonics and optoelectronics

Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.

Better routing for wireless networks

1 min read · Mon, Jul 27 2015

sensors electrical engineering

An improved wireless communication protocol enables more robust links between remote electronic devices.

Keeping pollution under control

1 min read · Sat, Nov 5 2016

Sophisticated statistical methods can detect ozone pollution hot spots and monitor instrument failure.

monitoring sensors

Tiny, fast, accurate technology on the radar

1 min read · Wed, Apr 17 2019

communications electrical engineering sensors

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

Geospatial Data Science for Public Health Surveillance

statistical methods geospatial data analysis health surveillance Public Health spatio-temporal data analysis

Nov 20, 12:00 - 13:00

B9 L2 R2325

This talk will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance, highlighting challenges related to data biases and availability.

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

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

Nov 5, 17:15 - 18:15

B2 L5 R5220

spatio-temporal data analysis statistics

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

Spatial data fusion clustering algorithms spatio-temporal data analysis

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

mathematical modelling visualization computational predictions spatio-temporal data analysis applied 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.

A shock to the system

1 min read · Wed, Jul 22 2020

Spectral Analysis numerical analysis COVID-19 research

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

Biostatistics Spectral Analysis Neuroimaging

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

Time Series Spectral Analysis Wavelets Longitudinal Models Mixed Effects Models Functional Magnetic Resonance Imaging Electroencephalograms

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

Farah Abdulrazak Gomawi

Ph.D. Student, Statistics

Time Series Neuroimaging Electroencephalograms Functional Near-Infrared Spectroscopy Spectral Analysis Biostatistics

Farah Abdulrazak Gomawi is a Ph. D. student in Statistics at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Hernando Ombao. Education and Early Career Farah Abdulrazak Gomawi received her Bachelor of Science in Applied Mathematics from the University of California, Los Angeles (UCLA) in the United States, with the KAUST Gifted Student Program (KGSP) scholarship in 2023. Prior to this, she was a visiting student in the Biostatistics group in the summer of 2022. Research Interest Farah's research interests include time series analysis, machine

Statistical Models and Methods Based on Stochastic Partial Differential Equations

SPDEs non-Gaussian random fields statistical analysis Metric graphs

Mar 23, 14:00 - 15:30

B9 L2 R2322; Zoom Meeting ID 93209452060

This talk presents an overview of our research on statistical methods using stochastic partial differential equations (SPDEs), focusing on non-Gaussian random fields and fractional-order SPDEs, and theory for random fields and statistical analysis on metric graphs, highlighting theoretical contributions, software development, and applications relevant to KAUST RDI pillars.

Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

Carolina D. Euan Campos, Postdoctoral Research Fellow, Statistics

Nov 28, 12:00 - 13:00

B9 H2 R2325

solar energy solar irradiance analysis nonlinearity TAR model statistical analysis forecasting

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

Flexible Multivariate, Spatial, and Causal Models for Extremes

Yan Gong, Ph.D. Student, Statistics

Mar 28, 16:00 - 19:00

B4 L5 R5220

risk assessment statistical analysis

Risk assessment for natural hazards and financial extreme events requires the statistical analysis of extreme events, often beyond observed levels. The characterization and extrapolation of the probability of rare events rely on assumptions about the extremal dependence type and about the specific structure of statistical models. In this thesis, we develop models with flexible tail dependence structures, in order to provide a reliable estimation of tail characteristics and risk measures. Our novel methodologies are illustrated by a range of applications to financial, climatic, and health data.

Xiaotian Jin

Ph.D. Student, Statistics

Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization

Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.

Building bridges between mathematical statistics and real-world impact

4 min read · Tue, Jan 6 2026

Spotlight News Research in Focus

mathematical statistics random fields stochastic partial differential equations computational statistics

A research journey that connects statistical theory with the complexity of real-world systems.

Simon Nik

Postdoctoral Research Fellow, Statistics

Statistics of extremes extreme-value theory stochastic partial differential equations discrete time series

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

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

MCMC Particle Methods Stochastic Control machine learning stochastic partial differential equations

Al-Khawarzmi Distinguished Professor, Statistics

mathematical modelling visualization computational predictions spatio-temporal data analysis applied 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.

Mathematical modeling of the stochastic climate system

Georgiy L. Stenchikov, Emeritus Professor (former), Earth Science and Engineering

Feb 6, 12:00 - 13:00

B9 L2 H1

weather climate projections

Author of more than 290 journal and conference publications, Professor Stenchikov's research interests are in multi-scale modeling of environmental processes and numerical methods; global climate change, climate downscaling, atmospheric convection; assessment of anthropogenic impacts and geoengineering; air-sea interaction, evaluating environmental consequences of catastrophic events like volcanic eruptions, nuclear explosions, forest and urban fires; and air pollution, transport of aerosols, chemically and optically active atmospheric tracers, their radiative forcing and effect on climate.

A safe sweep of weather data

1 min read · Sat, Feb 25 2017

weather statistics applied mathematics

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

Revealing hidden relationships in data

1 min read · Sun, Jan 28 2018

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

statistics weather

Challenging statistics of weather extremes

1 min read · Sun, Feb 25 2018