Topics

<> LiDAR (3) <> LLM (3) <> Localization (3) <> mathematical modelling (3) <> MATLAB (3) <> Metric graphs (3)

Gaussian Random Fields on Metric Graphs

-

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.

Statistical Models and Methods Based on Stochastic Partial Differential Equations

-
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.

Vaibhav Mehandiratta

Postdoctoral Research Fellow (former), Statistics
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.
<> multivariate analysis (3) <> Nonlinear Optics (3) <> optimal control (3)

Salim Ksous

Epidemiological modeling and optimal control

-

KAUST

Compartmental epidemiological models are one of the simplest models for the spread of a disease. They are based on statistical models of interactions in large populations and can be effective in the appropriate circumstances. Their application historically and in the present pandemic has sometimes been successful and sometimes spectacularly wrong. In this talk I will review some of these models and their application. I will also discuss the behavior of the corresponding dynamical systems, and discuss how the theory of optimal control can be applied to them. I will describe some of the challenges in using such a theory to make decisions about public policy.

Raul Tempone

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.
<> proteins (3) <> reliability testing (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

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

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

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

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> reproducibility issues (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

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

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

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

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> scientific computing (3)

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

-

B9, L2, R2322

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

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
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

-

B9 L2 R2322

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.
<> scientific enterprise (3)

Reproducibility in Computer Science - Part 1

-

B9 L3 R3223

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

Reproducibility in Computer Science - Part 2

-

B9 L3 R3223

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

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
<> simulation (3) <> solar energy (3)

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

-

B9 H2 R2325

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the

Securing future energy supply

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

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.
<> statistical analysis (3)

Statistical Models and Methods Based on Stochastic Partial Differential Equations

-
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

-

B9 H2 R2325

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the

Flexible Multivariate, Spatial, and Causal Models for Extremes

-

B4 L5 R5220

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.
<> Statistical computing (3) <> supercomputing (3) <> Survival analysis (3) <> wireless communication (3) <> wireless networks (3) <> 5G and beyond (2) <> Advanced Perception (2) <> AI4Science (2) <> air pollution (2) <> Applied Machine Learning (2) <> Artificial Neural Network (2) <> Automous Vehicles (2) <> Bayesian Estimation (2) <> biology (2) <> biomedical devices (2) <> cancer (2) <> climate data (2) <> climate projections (2) <> combustion (2) <> communication (2) <> communication networks (2) <> Computational finance (2) <> computational fluids mechanics (2) <> Computational Neuroscience (2) <> computational probability (2) <> computational simulations (2) <> Cross-layer protocol design (2) <> data mining (2) <> Deep generative models (2) <> Differential privacy (2) <> Digital signal processing (2) <> drones (2) <> ecology (2) <> energy security (2) <> Energy-efficient wireless multihop networks (2) <> extreme environmental events (2) <> Federated learning (2) <> forecasting (2)

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

-

B9 H2 R2325

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the

Cristian Felipe Jiménez Varón

Ph.D. Student, Statistics
Cristian Felipe Jiménez Varón is a Ph.D. candidate in the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Cristian obtained a master's degree in applied mathematics from the Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. He also holds dual bachelor's degrees in industrial engineering and chemical engineering from UNAL. Research Interests Cristian's research interests include the analysis of large volumes of data, statistical modeling and environmental statistics.
<> FPGA (2) <> Free-space optical communications (2) <> Functional Time Series (2) <> Generative AI and LLMs (2) <> GPU Computing (2) <> graphical models (2) <> health (2) <> landslide susceptibility modeling (2)

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

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

Luigi Lombardo

Postdoctoral Research Fellow, Statistics
Dr. Luigi Lombardo joined the Extreme Statistics Research Group and worked as Postdoctoral Research Fellow from April, 2016, to December, 2018, under the joined supervision of Prof. Raphaël Huser and Prof. Martin Mai. After his postdoc at KAUST, Luigi moved to ITC, University of Twente, Netherlands, where he embraced an academic career by becoming an Assistant Professor of Soil Science. See his personal website here. Education and early career Luigi Lombardo received his Ph.D. from Universita’ degli Studi di Palermo, Palermo, Italy in 2015. He joined KAUST as a postdoc in April 2016, and moved
<> Large Language Models (2) <> laser (2) <> latent Gaussian models (2) <> Latent variable models (2) <> living systems (2) <> machine intelligence (2) <> machine learning for wireless communication (2) <> macroeconomics (2) <> Mathematical modeling (2) <> mathematical statistics (2) <> mathematics (2) <> mean-field games (2) <> ML (2) <> multimodal sensing (2) <> Multivariate extremes (2) <> multivariate spatial processes (2) <> multivariate statistics (2) <> nanomaterials (2) <> Network Design (2) <> neural network (2) <> Neural Networks (2) <> Neuroimaging (2) <> Non-Terrestrial Networks (2) <> oceanography (2) <> optical computing (2) <> optimal experimental design (2) <> photonic integrated circuits (2) <> point processes (2) <> probability theory (2) <> Programming language (2) <> Public Health (2) <> quantile regression (2) <> Quantitative finance (2) <> quantum communication (2) <> quantum computing (2) <> R-INLA (2) <> Radio Frequency Integrated Circuits (2)