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.
Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026 Apr 19 - 29, All day Auditorium 0215 between B4 & B5 Workshops stochastic algorithm numerical analysis statistical learning Bringing together experts in stochastic algorithms, numerical analysis and statistical learning with applications in climate modeling and quantitative finance.
How Machines Explore, Conjecture, and Discover Mathematics Sebastian Pokutta, Vice President, Zuse Institute Berlin (ZIB); Professor, Technische Universität Berlin (TU Berlin) Apr 9, 12:00 - 13:00 B9 L2 R2325 machine learning artificial intelligence AI optimization Structures In this talk, we illustrate mathematical research developing methodologies that combine optimization, machine learning, and mathematical structure to navigate large, complex, and highly constrained search spaces with a focus on the Hadwiger–Nelson problem: a long-standing open problem in discrete geometry and extremal combinatorics concerning colorings of the plane without monochromatic unit-distance pairs.
Free-Space Optics for Non-Terrestrial Networks: Energy-Sustained Platforms and Photon-Counting Receivers Heyou Liu, Ph.D. Student, Electrical and Computer Engineering Apr 1, 13:30 - 15:00 B5 R5209 wireless communication systems Free Space Optics Non-Terrestrial Networks Stochastic Geometry This dissertation develops an integrated NTN–FSO design framework spanning energy-sustained platform deployment, turbulence-aware laser power transfer (LPT) for in-flight recharging, and photon-counting receiver optimization under dead time.
Engineering Scalable Multi-Robot Systems for Reliable Operation Under Uncertainty and Resource Constraints Dr. Mohamed Salaheddine Talamali, Research Associate, Energy Aware Swarm Programming, School of Electrical and Electronic Engineering (EEE), University of Sheffield (TUOS) Mar 29, 12:00 - 13:00 B4/5 A0215 This talk presents a constraint-driven engineering perspective on achieving reliable operation in scalable multi-robot systems under uncertainty and resource constraints.
Model Predictive Control and Imitation Learning Algorithms for Robot Motion Planning in Physical Human-Robot Interaction Aigerim Nurbayeva, Postdoctoral Research Fellow, Electrical and Computer Engineering Mar 15, 12:00 - 13:00 B9 R2325 Model Predictive Control robotics deep neural networks Numerical Optimization universal robot human-robot interactions NMPC This seminar presents a framework for safe and efficient human-robot workspace sharing by using Deep Neural Networks (DNN) and safety filters to rapidly imitate computationally heavy Nonlinear Model Predictive Control method (NMPC), with successful experimental validation on a UR5 manipulator.
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.
Advances in Multiscale Hierarchical Decomposition Methods for Image Restoration Luminita Vese, Professor of Mathematics, University of California, Los Angeles (UCLA) Mar 10, 14:30 - 15:30 B1 L3 R3119; Zoom Meeting 99254591389 This talk will present the multiscale hierarchical decomposition method (MHDM) for image restoration and scale separation, building on the framework introduced by Tadmor, Nezzar, and me.
Mathematical Modelling of Solar Cells Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC) Mar 10, 11:00 - 12:30 B1 L3 R3119 solar cells Mathematical modeling PDEs numerical PDEs This talk presents the development and the numerical approximation of mathematical models for some well known solar cell architectures.
Provable and Measurable Machine Unlearning in Modern Learning Systems Cheng-Long Wang, Ph.D. Student, Computer Science Mar 10, 10:30 - 12:30 B2 L5 R5209 Machine Unlearning Data Privacy Trustworthy AI Federated learning machine learning AI This dissertation examines the foundations of machine unlearning under realistic learning system constraints and proposes both theoretically grounded unlearning algorithms and principled evaluation frameworks for modern learning systems.
Learning to Identify and Exploit Neural Network Dynamics in Multi-Step Inference Haozhe Liu, Ph.D. Student, Computer Science Mar 9, 13:30 - 15:30 B4 L5 R5220; Zoom Meeting 95866424218 This dissertation studies the temporal dynamics of multi-step inference and reveals that contributions across steps are sparse and uneven.
Extreme Computing Universals David Keyes, Professor, Applied Mathematics and Computational Science Mar 9, 12:00 - 13:00 B9 L2 R2325 HPC APIs extreme computing smart systems parallel computing software development This talk redefines "extreme" computing as operating under severe resource constraints rather than just massive scale, outlining universal algorithmic, hardware, and system-level strategies to overcome these challenges, illustrated by KAUST success stories.
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.
Simulation of Metasurfaces Described by Generalized Sheet Transition Conditions Using Integral Equations Sebastian Celis Sierra, Postdoctoral Research Fellow, Electrical and Computer Engineering Mar 8, 00:00 - 01:00 B9 L2 R2325 Metasurfaces computational electromagnetics Computer simulations numerical integration This seminar outlines the development of computationally efficient integral equation solvers that simulate complex multiscale metasurfaces by modeling their physical geometries as infinitesimally thin sheets governed by generalized sheet transition conditions, thereby avoiding the need for full volumetric discretization.
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.
Approximation and Optimization for Neural Networks Gerrit Welper, Assistant Professor, Mathematics, University of Central Florida (UCF) Mar 4, 16:00 - 17:00 Zoom Meeting 95807131415 Neural Networks optimization deep neural networks Finite element methods In this talk, we consider new connections between the approximation and optimization of neural networks. Instead of relying on excessive over-parametrization to achieve zero training loss, we identify good minima by comparison with established approximation bounds.
Assessing Network Middlebox Impact on End-to-End Protocol Behavior via a Distributed and Reprogrammable Framework Ilies Benhabbour, Ph.D. Student, Computer Science Mar 4, 13:00 - 16:00 B5 L5 R5220 cybersecurity Cryptography distributed computing This dissertation focuses on the detection and verification of network middleboxes thanks to the creation of a new distributed framework called NoPASARAN.
Structure Preserving Methods for Schrodinger Type of Equations Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC) Mar 3, 14:30 - 15:30 B1 L3 R3119 Schrödinger equation cosmology waves This talk presents a class of structure preserving methods for Schrodinger type of equations with applications in the generation of rogue waves and cosmology.
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.
From Prompts to Production: The Systems Agenda for Agentic AI Marco Canini, Professor, Computer Science Mar 2, 12:00 - 13:00 B9 L2 R2325 AI observability reproducibility Computer Information Systems In this talk, I will discuss our recent work on benchmarking, evaluation, and deployment of multi-agent LLM systems, and use it to outline a broader research agenda for agentic AI as a systems discipline - where progress depends not only on better models, but on principled infrastructure for observability, reproducibility, safe experimentation, and scalable execution.
From Edge to Intelligence: Light-Driven Synaptic Devices for Adaptive Healthcare and Vision Systems Dayanand Kumar, Postdoctoral Research Fellow, Electrical and Computer Engineering Mar 1, 12:00 - 13:00 B9 L2 R2325 optoelectronic devices light-driven synaptic devices neuromorphic electronics edge computing This talk presents a flexible, back-end-of-line compatible optoelectronic synapse developed for neuromorphic edge computing.
Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective Tan Bui-Thanh, Professor, Endowed William J. Murray, Jr. Fellow in Engineering No. 4, Oden Institute for Computational Engineering & Sciences, Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin (UT Austin) Feb 26, 14:30 - 15:30 B1 L3 R3119 Scientific Machine Learning SciML Scientific Deep Learning SciDL deep learning machine learning digital twins uncertainty quantification Computational mathematics This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.
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.
Towards Scalable and Structured Understanding in Visual LLMs Mohamed Elhoseiny, Associate Professor, Computer Science Feb 23, 12:00 - 13:00 B9 L2 R2325 LLM Visual Language Models VLMs visual computing In this talk, we explore a suite of recent advances toward scalable, structured video comprehension using Large Vision Language Models (Video LLMs).
Computing Heteroclinic Orbits for Dynamical Systems Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC) Feb 23, 11:00 - 12:30 B1 L3 R3119 Dynamical Systems heteroclinic orbits Numerical Modeling This course provides an overview of the most well known methods for computing heteroclinic orbits for dynamical systems.
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.
A Dichotomy of Continuous Finite Element Spaces and Its Application to Energy-Conservative Galerkin Methods for Nonlinear and Dispersive Wave Equations Dimitrios Mitsotakis, Reader/Associate Professor, Engineering Mathematics, School of Mathematics and Statistics, Victoria University of Wellington (VUW) Feb 17, 14:30 - 15:30 B1 L3 R3119 dispersive waves optimization Finite elements This talk illustrates why energy-conservative Galerkin methods for nonlinear and dispersive wave equations achieve optimal convergence rate with odd-degree polynomials.
Eyes in the Sky: AI-Based Camel Identification and Tracking Using Drones Basem Shihada, Program Chair, Computer Science Feb 16, 12:00 - 13:00 B9 L2 R2325 AI drones animal tracking In this talk, I present a low-cost, AI-powered drone system capable of recognizing and tracking camels from the air.
Tapping Into the Full Potential of the Stratosphere Mohamed-Slim Alouini, Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering Feb 15, 12:00 - 13:00 B9 L2 R2325 HAPSs Large-scale Connectivity communication technology FSO systems Free-space optical communications optical connectivity This talk examines how High-Altitude Platform Stations (HAPS) leverage intelligent beam management and optical links to democratize broadband access and provide resilient solutions for disaster recovery.
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.
Mathematical Design and Analysis of Iterative Methods for Linear and Nonlinear Problems Jongho Park, Research Scientist, Applied Mathematics and Computational Science Feb 10, 14:30 - 15:30 B1 L3 R3119 nonlinear models algorithms iterative methods This talk presents a systematic approach to the design and analysis of iterative methods for solving linear and nonlinear problems.
Accelerating Branch-and-Bound Graph Algorithms with GPUs Izzat El Hajj, Assistant Professor, Computer Science, American University of Beirut (AUB) Feb 9, 12:00 - 13:00 B9 L2 R2325 parallel computing optimization GPU Algorithms HPC Graph Theory Efficient 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.
Rising Stars in AI Symposium 2026 Feb 9, 08:00 - 17:00 KAUST Campus AI artificial intelligence Join the AI research community for a multi-day symposium focused on emerging research directions and collaboration.
AI for Optics and Optics for AI Wolfgang Heidrich, Professor, Computer Science Feb 8, 12:00 - 13:00 B9 L2 R2325 This talk will highlight recent work on the interconnection between AI techniques and (imaging) optics.
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.
Efficient Machine Learning for Scientific and Medical Applications Yasir Ghunaim, Ph.D. Student, Computer Science Feb 4, 18:00 - 20:00 B4/5 L0 A0215 Efficient Machine Learning machine learning Graph Neural Networks This dissertation addresses key challenges of machine learning in scientific and medical domains by developing methods that improve model efficiency, data efficiency, and learning under real-world constraints.
From Dialects to Peptides: Scalable and Efficient AI for People Muhammad Abdul-Mageed, Canada Research Chair, Natural Language Processing and Machine Learning; Associate Professor, School of Information, Department of Linguistics, The University of British Columbia Feb 2, 12:00 - 13:00 B9 L2 R2325 AI Efficient Efficient Machine Learning This talk presents a unified AI framework for decoding complex human and biological signals - spanning African and Arabic dialects to proteomics - by prioritizing rigorous measurement, cultural competence, and computational efficiency to ensure global scalability and accessibility.
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.
Uncertainty Quantification with Conformal Prediction in Energy Data Tarek AlSkaif, Associate Professor, Energy Informatics, Wageningen University (WUR) Feb 1, 12:00 - 13:00 B9 L2 R2325 conformal prediction machine learning uncertainty quantification The talk will introduce the fundamentals of conformal prediction (CP) - a flexible, model-agnostic uncertainty quantification framework for generating statistically valid uncertainty estimates in energy applications - and demonstrate how it can be layered on top of machine learning models to produce reliable prediction intervals.
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.
The Sharpness Condition for Constructing Finite Element From a Superspline Qingyu Wu, Ph.D. Student, Mathematics, Peking University Jan 28, 14:00 - 15:00 B1 L3 R3119 In this talk, I will discuss the sharpness conditions for constructing Cʳ conforming finite element spaces from superspline spaces on general simplicial triangulations and introduce the concept of extendability for pre-element spaces, which unifies both superspline and finite element spaces under a common framework.
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.
Thoughts About Machine Learning Jürgen Schmidhuber, Professor, Computer Science Jan 26, 15:30 - 17:30 B9, Lecture Hall 1, R-2322 AI machine learning deep learning A weekly seminar series exploring advanced AI concepts that go beyond the scope of standard deep learning courses.
Shedding New Light on the Past: Applications of Multi-Light Image Collections in Cultural Heritage Ruggero Pintus, Senior Researcher, Center for Advanced Studies, Research and Development in Sardinia (CRS4) Jan 26, 12:00 - 13:00 B9 L2 R2325 interactive visualization image processing data acquisition This talk provides an overview of developing advanced tools for the digitization and exploration of Cultural Heritage assets, with a deep dive into Multi-Light Image Collections (MLICs) at Visual and Data Intensive Computing group at CRS4 (Italy).
KAUST Research Conference on Mathematical and Data Sciences Jan 26 - 28, All day KAUST Campus data science scientific computing Computer science Theory and applications of data science.
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.
Ultrawide Bandgap Nano Devices Glen Isaac Maciel García, Ph.D. Student, Applied Physics Jan 25, 12:00 - 13:00 B9 L2 R2325 Ultrawide bandgap materials nano-engineering This seminar explores how nanoscale device architectures can unlock new functionalities in ultrawide bandgap (UWBG) materials beyond conventional applications.
Programmable Wavefunction Dynamics for Topological and Nonlinear Acoustic Control Ze-Guo Chen, Associate Professor School of Advanced Manufacturing, Nanjing University, China Jan 21, 10:00 - 11:00 B1 L3 R3119 topological phenomena This talk presents a unified route to topological and nonlinear acoustic control through engineered time-dependent modulation of on-site parameters, couplings, gain/loss, and external driving.
Causal Representation Learning Ali Tajer, Professor, Electrical, Computer, and Systems Engineering Jan 15, 15:00 - 16:00 B1 L2 2202 machine learning causal representation learning statistical learning Information theory In this talk, we will explore the latest advancements in the emerging field of causal representation learning (CRL).
Isotropic Geometry and Applications in Geometric Computing Khusrav Yorov, Ph.D. Student, Applied Mathematics and Computational Science Jan 15, 10:30 - 12:00 B3 L5 R5209 Isotropic Geometry geometry processing discrete differential geometry This thesis addresses challenges in computational design and fabrication, particularly in the geometry of architectural gridshell structures and their approximation, by solving nonlinear optimization problems.
