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, 11:00 - 12:00 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).
The Earliest Arrival of Quantum Advantage David Keyes, Professor, Applied Mathematics and Computational Science Apr 10, 12:00 - 13:00 B9 L2 R2325 quantum computing quantum processing unit supercomputers This talk outlines a "quantum first" strategy for future supercomputers, integrating QPUs, GPUs, and CPUs to optimize energy efficiency and accelerate scientific computing while addressing the current challenges and projecting the maturation of quantum computing by leveraging classical supercomputing advancements.
MI is All You Need: Understanding Complex Multivariate Systems Through the Lenses of GenAI Pietro Michiardi, Professor, Department Head, Data Science Department, EURECOM Mar 26, 12:00 - 13:00 B1 L4 R4102 This talk explores the use of score functions and denoising neural networks for efficient estimation of mutual information and novel extensions to information theory that describe complex multivariate interactions, demonstrating applications in automotive sensing, semantic alignment, and neuroscience.
Statistical Models and Methods Based on Stochastic Partial Differential Equations David Bolin, Professor, Statistics Mar 23, 14:00 - 15:30 B9 L2 R2322; Zoom Meeting ID 93209452060 SPDEs non-Gaussian random fields statistical analysis Metric graphs 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.
Latent Abstractions, Mutual Information and Generative Diffusion Models Giulio Franzese, Assistant Professor, Data Science Department, EURECOM Mar 23, 12:00 - 13:00 B1 L4 R4102 This talk presents a mathematical framework linking stochastic differential equations and information theory on how diffusion models utilize latent abstractions via implicit stochastic filtering to synthesize high-dimensional data.
Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining Mikhail Moshkov, Professor, Applied Mathematics and Computational Science Mar 20, 12:00 - 13:00 B9, L2, R2325 Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.
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.
One-on-One Statistical Consulting for KAUST Researchers Mar 11, 10:00 - 16:00 B1 L4 R4102 Join the American Statistical Association (ASA) KAUST Student Chapter for one-on-one statistical consultations.
The Ball-Proximal (=“Broximal”) Point Method: a New Algorithm, Convergence Theory, and Applications Kaja Gruntkowska, PhD student, Statistics, KAUST Mar 6, 12:00 - 13:00 B9, L2, R2325 Non-smooth and non-convex global optimization poses significant challenges across various appli- cations, where standard gradient-based methods often struggle. We propose the Ball-Proximal Point Method, Broximal Point Method, or Ball Point Method (BPM) for short – a novel algorith- mic framework inspired by the classical Proximal Point Method (PPM) (Rockafellar, 1976), which, as we show, sheds new light on several founda- tional optimization paradigms and phenomena, including non-convex and non-smooth optimiza- tion, acceleration, smoothing, adaptive stepsize selection, and trust-region methods.
Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity Arto Maranjyan, Ph.D. Student, Computer Science Feb 27, 12:00 - 13:00 B9, L2, R2325 Asynchronous Stochastic Gradient Descent (Asynchronous SGD) is a cornerstone method for parallelizing learning in distributed machine learning. However, its performance suffers under arbitrarily heterogeneous computation times across workers, leading to suboptimal time complexity and inefficiency as the number of workers scales.
Derivative-Free Methods in Nonconvex Optimization with and Without Noise Boris Mordukhovich, Distinguished University Professor, Wayne State University Feb 20, 12:00 - 13:00 B9, L2, R2325 This talk addresses the study of nonconvex derivative-free optimization problems, where only information of either smooth objective functions or their noisy approximations is available. General derivative-free methods are proposed for minimizing differentiable (not necessarily convex) functions with globally Lipschitz continuous gradients, where the accuracy of approximate gradients is interacting with stepsizes and exact gradient values.
Recent Advances in Infinity Laplacian Equations Aelson Sobral, Postdoctoral Research Fellow, Applied Mathematics and Computational Science Feb 13, 12:00 - 13:00 B9, L2, R2325 The infinity Laplacian is a highly degenerate, nonlinear, second-order operator that arises in the study of optimal Lipschitz extensions and tug-of-war games, where two players alternately choose directions to move. In this presentation, we will provide a comprehensive motivation for key aspects of the infinity Laplacian and report on recent advances in the qualitative properties of its evolutionary counterpart.
A Limiting Model for MEMS with Heterogeneous Dielectric Properties Katerina Nik, Assistant Professor, Applied Mathematics and Computational Science Feb 6, 12:00 - 13:00 B9, L2, R2325 Idealized microelectromechanical systems (MEMS) consist of two dielectric plates: a rigid ground plate above which an elastic plate is suspended. The elastic plate deforms due to a Coulomb force induced by a voltage difference applied between the two components. Coating the ground plate with an insulating layer prevents direct contact between the plates.
Singular Points of Solutions of Hamilton-Jacobi Equations Melih Ucer, Postdoctoral Research Fellow, Applied Mathematics and Computational Science Jan 30, 12:00 - 13:00 B9, L2, R2325 The Hamilton-Jacobi equation is a prototypical nonlinear first-order PDE which appears in various settings including dynamical systems, optimal control, differential games, etc. where some action/value function solves this PDE. However, these action/value functions are often not smooth on the entire domain, and indeed the Hamilton-Jacobi equation often does not admit globally smooth solutions.
Improving Optimization of Likelihood-based Generative Models with One Line of Code Maurizio Filippone, Associate Professor, Statistics Dec 5, 12:00 - 13:00 B9, L2, R2325 Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images.
LoCoDL: Communication-Efficient Distributed Optimization with Local Training and Compression Laurent Condat, Senior Research Scientist, Computer Science Nov 27, 12:00 - 13:00 B9, L3, R3125 In distributed optimization, and even more in federated learning, communication is the main bottleneck. We introduce LoCoDL, a communication-efficient algorithm that leverages the two techniques of Local training, which reduces the communication frequency, and Compression with a large class of unbiased compressors that includes sparsification and quantization strategies.
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.
The KAUST 2024 Workshop on Statistics Nov 18, 09:00 - Nov 21, 17:00 Auditorium between Building 4 - 5 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.
Deep Neural Networks for Large-scale Complex Spatial and Spatio-temporal Processes Pratik Nag , Ph.D. Student, Statistics May 29, 09:00 - 11:30 B4 L5 R5220 Environmental statistics play a critical role in various interconnected domains, encompassing weather and climate forecasting, air quality monitoring, and sustainable urban planning. However, because of their high inherent unpredictability and nonstationarity, modeling complex spatio-temporal dynamics of environmental processes is challenging. This dissertation develops a set of DNN based methods for large-scale spatial and spatio-temporal processes.
Visualization, Characterization, and Forecasting of Multivariate and Functional Time Series Cristian Felipe Jiménez Varón, Ph.D. Student, Statistics May 22, 14:00 - 16:00 B4 L5 R5220 This thesis explores advanced statistical models and methods for analyzing multivariate and functional time series data. It focuses on various aspects of statistical analysis, including visualization, robust outlier detection, inference, and forecasting. It addresses challenges in outlier detection for functional data, quantile spectral estimation for multivariate time series, and high-dimensional functional time series forecasting, with applications in environmental, financial, and demographic fields.
Intracellular "in silico microscopes" - Fully 3D Spatio-Temporal Virus Replication Model Simulations Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science May 9, 12:00 - 13:00 B9 L2 H2 H2 Despite being small and simple structured in comparison to their victims, virus particles have the potential to harm severly and even kill highly developed species such as humans. To face upcoming virus pandemics, detailed quantitative biophysical understanding of intracellular virus replication mechanisms is crucial.
Statistical Deep-Learning for Spatiotemporal Extremes Raphaël Huser, Associate Professor, Statistics May 2, 12:00 - 13:00 B9 L2 H2 H2 Rare, low-probability events often lead to the biggest impacts. Therefore, the development of statistical approaches for modeling, predicting and quantifying environmental risks associated with natural hazards is of utmost importance. In this seminar, I will show how statistical deep-learning methods can help solve challenges that arise when modeling complex and massive spatiotemporal extremes data.
Statistical Analysis of Topological Patterns in Dependence Networks of Brain Time Series Data Anass B. El-Yaagoubi, Postdoctoral Research Fellow, Statistics May 2, 11:00 - 12:00 B1 L4 R4102 Given the complex nature of brain signals and the challenges involved in estimating its dependence and analyzing the emerging topological patterns, this dissertation introduces innovative statistical tools designed to explore both the functional and effective connectivity within brain networks. It sheds light on frequency-specific patterns in ADHD subjects and introduces a novel approach for examining the hierarchical structure of brain regions during seizures. Our work provides a novel perspective on the organization of brain networks and presents insight into how various conditions influence their complex structure.
