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.
Towards an Early Warning System for Climate-Sensitive Infectious Diseases: Spatio-Temporal Modeling and Deep Learning for Dengue Forecasting in Brazil Xiang Chen, Ph.D. Student, Statistics Apr 30, 13:00 - 16:00 B5 R5209 spatio-temporal modeling geospatial statistics infectious disease explainable AI Public Health climate data deep learning computational predictions 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.
Robust and Fuzzy Methods for High-Dimensional Time Series Clustering and Forecasting Ziling Ma, Ph.D. Student, Statistics Apr 30, 12:00 - 13:00 B9 R2325 These talks introduce several robust methods for clustering and forecasting multivariate time series data.
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.
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
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).
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.
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.
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.
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.
A Unified Monotonicity Framework for Mean-Field Games Rita A. Ferreira, Research Scientist, Mean-field Games and Nonlinear PDE Apr 2, 12:00 - 13:00 B9 R2325 gamma-convergence asymptotic analysis variational analysis Variational mean-field games In this talk, we discuss how monotone operator methods provide a unified approach to existence, uniqueness, and regularity in MFGs.
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.
Geospatial Data Science for Public Health Surveillance Paula Moraga, Assistant Professor, Statistics Mar 9, 11:15 - 12:45 B4/5 L0 A0215; Zoom Meeting 94713495879 Geospatial Data Public Health Public health surveillance geospatial statistics statistical methods 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.
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.
Bayesian Inference for Partially Observed Continuous-Time Processes Amin Wu, Ph.D. Student, 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.
Graphpcor: Prior for Correlation Matrices Elias Teixeira Krainski, Research Scientist, Statistics Feb 26, 12:00 - 13:00 B9 L2 R2325 correlation Bayesian Estimation expert knowledge integration This talk introduces a scalable, graph-based framework for modeling correlation matrices that integrate expert-informed priors.
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.
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.
On the Modeling and Approximation of Phase Transitions in Elasticity Georgios Grekas, Postdoctoral Research Fellow, Applied Mathematics and Computational Science Feb 5, 12:00 - 13:00 B9 L2 R2325 Phase transitions elasticity mathematical modelling This talk explores the mathematical modeling of phase transitions in elasticity, drawing motivation from observed phenomena in crystalline solids and biomaterials.
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.
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.
Beyond Bayesian Uncertainty: a Variational Perspective Chérief-Abdellatif Badr-Eddine, CNRS (Chargé de Recherche) Researcher, Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Sorbonne Université, France Jan 28, 12:00 - 13:00 B4/5 L0 A0215 In this talk, I will explore how the statistics and machine learning communities are expanding the frontiers of uncertainty quantification beyond traditional Bayesian frameworks.
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.
Tales of a Spur Hunter: The Wandering Spur Michael Peter Kennedy, Full Professor, School of Electrical and Electronic Engineering, University College Dublin Dec 7, 12:00 - 13:00 B9 L2 R2325 Prof. Michael Peter Kennedy presents the decade-long journey to diagnose and fix 'wandering spurs' in frequency synthesizers.
Will AI Replace Professors? Pavel Pevzner, Ronald R. Taylor Chair and Distinguished Professor, Computer Science and Engineering, University of California, San Diego Dec 4, 12:00 - 13:00 B2/B3 L0 A0215 This talk explores Massive Adaptive Interactive Texts (MAITs) as a pioneering AI technology that aims to replace the one-size-fits-all lecture model with a responsive and scalable system for individualized instruction.
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.
Finite Element Approximation of Eigenvalue Problems in Mixed Form Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering Nov 27, 12:00 - 13:00 B9 L2 R2325 This talk will discuss the finite element approximation of the eigenvalues associated with the Maxwell system.
Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering
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.
Geospatial Data Science for Public Health Surveillance Paula Moraga, Assistant Professor, Statistics Nov 20, 12:00 - 13:00 B9 L2 R2325 statistical methods geospatial data analysis health surveillance Public Health spatio-temporal data analysis 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.
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.
Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation Qilong Pan, Ph.D. Student, Statistics Nov 12, 09:00 - 11:00 B5 L5 R5209 statistics spatio-temporal statistics GPU Computing HPC 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.
The KAUST 2025 Workshop on Statistics Nov 2, 09:00 - Nov 6, 17:00 Between Building 2 / 3, Level 0, Auditorium 0215 The KAUST Statistics Workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, climate, energy and beyond.
Accelerating Branch-and-Bound Graph Algorithms with GPUs Izzat El Hajj, Assistant Professor, Computer Science, American University of Beirut Oct 30, 12:00 - 13:00 B9 L2 R2325 This talk presents multiple techniques that we have developed to load balance the search tree traversal on GPUs and mitigate the strain on memory capacity and bandwidth.
Unlocking Euclidean Problems with Isotropic Initialization Mikhail Skopenkov, Research Scientist, Computer Science Oct 30, 12:00 - 13:00 B9 L2 R2325 The seminar introduces a novel, general approach for solving challenging constraint systems in Euclidean geometry problems by leveraging analogous, structure-preserving simplifications found in isotropic geometry to initialize and guide optimization algorithms.
One-on-One Statistical Consulting for KAUST Researchers 2025 Oct 20, All day B1 L4 R4102 In recognition of World Statistics Day, a United Nations–designated observance that highlights the importance of trustworthy data and sound statistical methods, the KAUST Student Chapter of the American Statistical Association (ASA) is hosting one-on-one consulting sessions for KAUST students and researchers. Graduate students and postdoctoral researchers from the Statistics Program will be available to provide personalized guidance on research-related statistical challenges. These consultations aim to strengthen the use of statistical methods across disciplines, support high-quality research
Bias-Reduced Estimation of Structural Equations Models Haziq Jamil, Research Specialist, Statistics Oct 16, 12:00 - 13:00 B9 L2 R2325 This talk demonstrates that the reduced-bias M-estimation (RBM) framework is a computationally efficient and robust method for mitigating finite-sample bias in structural equation models, outperforming standard estimators, especially in small-sample contexts.
Block Low-Rank Matrices for Modern Scientific Computing Daria Sushnikova, Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering Oct 9, 12:00 - 13:00 B9 L2 R2325 Block low-rank matrices provide framework for compressing and accelerating large-scale computations. In this talk, I will introduce the basic principles behind these matrix formats, highlight notable algorithms that exploit their structure, and discuss their growing role in modern computational mathematics. Applications ranging from seismic imaging and computational biology to artificial intelligence will be used to illustrate the broad impact of block low-rank methods on science and engineering.
Daria Sushnikova, Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering
Efficient Bayesian Methods for Biostatistics Janet van Niekerk, Research Scientist, Statistics Oct 2, 12:00 - 13:00 B9 L2 R2325 bayesian methods In this talk, I will present some case studies where we approach near real-time inference for complex Biostatistics models, such as disease mapping and brain activation mapping models, among others often encountered in the biostatistics domain, using INLA.
Nonparametric Functional Quantiles, Skewness, and Probability Bands via the Functional Signed Directionality Emmanuel Ambriz, Postdoctoral Research Fellow, Statistics Sep 25, 12:00 - 13:00 B9 L2 R2325 functional signed directionality We propose the Functional Signed Directionality (FuSD), a nonparametric order for function-valued random elements that defines a pushforward distribution on the real line. This construction enables probabilistic inference in the functional domain: we define set-valued functional quantiles, introduce quantile-based summaries of functional spread and skewness, and construct tight probability bands that reflect the underlying functional law via the FuSD distribution.
Neural Methods for Amortized Inference with Models for Spatial Extremes Raphaël Huser, Associate Professor, Statistics Sep 18, 12:00 - 13:00 B9, L2, R2325 Neural Bayes estimators are neural networks that approximate Bayes estimators. Once trained, these estimators are not only statistically efficient, but also extremely fast to evaluate and amenable to rapid uncertainty quantification. Neural Bayes estimators thus have compelling advantages when used with spatial models that have a computationally intractable likelihood function, as often the case when modeling spatial extremes. In this talk, I will showcase the power of neural Bayes estimators for spatial extremes in a range of climate-related and geo-environmental data applications.
Gaussian Random Fields on Metric Graphs David Bolin, Professor, Statistics Sep 11, 12:00 - 13:00 B9 B2 L2325 Gaussian random fields Metric graphs Statistical Modeling 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.
From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 4, 12:00 - 13:00 B9 L2 R2325 AI machine learning optimization algorithms LLM 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.
Advanced Spatial Methods for Health Surveillance in Saudi Arabia: Data Integration and Cluster Detection Hanan Alahmadi, Ph.D. Student, Statistics May 29, 15:00 - 16:00 Building 5, Seaside, Level 5, Room 5209 Under the framework of Saudi Arabia’s Vision 2030, the Health Sector Transformation Program (HSTP) aims to revolutionize the healthcare sector by enhancing access to services, increasing their value, and bolstering preventive measures against health threats. This thesis presents innovative and efficient spatial modeling approaches tailored for public health surveillance in Saudi Arabia, including the modeling of hepatitis B and hepatitis C, leading causes of hepatocellular carcinoma and severe liver diseases which place a significant burden on Saudi Arabia’s healthcare system. Reducing the prevalence of these diseases is critical to achieving the health goals of Vision 2030, emphasizing health as a cornerstone of a vibrant society.
The Signed Translation Transformed Depth: Order, Quantiles, Spread, Skewness, and Quantile Regression for Multivariate Functional Data Emmanuel Ambriz, Postdoctoral Research Fellow, Statistics May 15, 12:00 - 13:00 B9 L2 R2325 multivariate analysis This seminar introduces the Signed Translation transformed Depth (STtD), a novel interpretable ordering method for multivariate functional data, which enables enhanced distributional analysis, the definition of descriptive tools, and a flexible vine copula-based quantile regression framework.
Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation Qilong Pan, Ph.D. Student, Statistics May 8, 12:00 - 13:00 B9 L2 R2325 machine learning Geospatial Data GPU Computing 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.
Current and Future Challenges and Solutions in AI & HPC System and Thermal Management Dr. Gamal Refai-Ahmed, Senior Fellow & Chief Architect, AMD Member of U.S. National Academy of Engineering Life Fellow, Canadian Academy of Engineering Fellow, Engineering Institute of Canada Fellow & Distinguished Lecturer, IEEE Life Fellow, ASME May 6, 13:00 - 17:00 B4 L5 R5209 Led by expert Dr. Gamal Refai Ahmed, this course explores innovative thermal management and packaging solutions for AI and HPC systems, addressing current and future challenges with cutting-edge techniques and next-generation design principles.
Data Centric Engineering: Hype or Transformation and Engineering the Future? Mark Girolami, Sir Kirby Laing Professor of Civil Engineering University of Cambridge, United Kingdom May 6, 12:00 - 13:00 B 2/3 L0 R 0215 This talk will highlight the role of recent advances in the Data Sciences and related Artificial Intelligence technologies and how they are transforming the study and practice of the natural, physical, and engineering sciences.
A Statistical Construction of the Finite Element Method Mark Girolami, Sir Kirby Laing Professor of Civil Engineering University of Cambridge, United Kingdom May 5, 12:00 - 13:00 B 2/3 L0 R 0215 This talk will present a formal statistical construction and mathematical analysis of the FEM which systematically blends both mathematical description with observational data and provides both small and large scale examples from 3D printed structures to working rail bridges currently operated by the United Kingdom Network Rail.
Retraction Maps, Feedback Linearization and Nonholonomic Integrators Ravi Banavar, Professor, Systems and Control Engineering, IIT Bombay May 1, 12:00 - 13:00 B9 L2 R2325 This talk will introduce the utility of retraction maps on Riemannian manifolds to two applications in applied mechanics and control.
Linear Solvers for Large-Scale Bayesian Modeling Lisa Gaedke-Merzhäuser, Postdoctoral Research Fellow, Statistics Apr 24, 12:00 - 13:00 B9 L2 R2325 In this talk we explore what it means to perform Bayesian inference and introduce the methodology of integrated nested Laplace approximations (INLA).
