Events

Nov 30 - Dec 6, 2025

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  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 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.

Nov 2 - Nov 8, 2025

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  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.

Oct 26 - Nov 1, 2025

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  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.

Oct 19 - Oct 25, 2025

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  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

Oct 12 - Oct 18, 2025

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  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.

Oct 5 - Oct 11, 2025

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  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.

Sep 28 - Oct 4, 2025

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  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.

Sep 21 - Sep 27, 2025

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  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.

Sep 14 - Sep 20, 2025

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  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.

Sep 7 - Sep 13, 2025

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  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.

Aug 31 - Sep 6, 2025

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  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.

May 25 - May 31, 2025

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  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.

May 11 - May 17, 2025

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  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.

May 4 - May 10, 2025

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  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.

Apr 27 - May 3, 2025

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  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.

Apr 20 - Apr 26, 2025

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  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).

Apr 6 - Apr 12, 2025

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  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.

Mar 23 - Mar 29, 2025

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  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.
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  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.

Mar 16 - Mar 22, 2025

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  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.

Mar 9 - Mar 15, 2025

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  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.
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  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.

Mar 2 - Mar 8, 2025

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  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.

Feb 23 - Mar 1, 2025

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  Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity

  Artavazd Maranjyan, PhD Student, AMCS, KAUST

  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.

Feb 16 - Feb 22, 2025

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  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.

Feb 9 - Feb 15, 2025

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  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.

Feb 2 - Feb 8, 2025

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  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.

Jan 26 - Feb 1, 2025

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  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.

Dec 1 - Dec 7, 2024

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  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.

Nov 24 - Nov 30, 2024

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  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.
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  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.

Nov 17 - Nov 23, 2024

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  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.

May 26 - Jun 1, 2024

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  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.

May 19 - May 25, 2024

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  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.

May 5 - May 11, 2024

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  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.

Apr 28 - May 4, 2024

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  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.
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  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.

Apr 21 - Apr 27, 2024

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  On Distribution-Free Risk Control

  Michael Jordan, Professor Emeritus, University of California, Berkeley

  Apr 24, 15:00 - 16:00

  B9 L4 R4225

  We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any underlying model and (unknown) data-generating distribution and do not require model refitting.
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  An Alternative View on AI: Collaborative Learning, Statistical Incentives, and Social Welfare

  Michael Jordan, Professor Emeritus, University of California, Berkeley

  Apr 23, 12:00 - 13:00

  Auditorium between B2 and 3

  Artificial intelligence (AI) has focused on a paradigm in which intelligence inheres in a single, autonomous agent. Social issues are entirely secondary in this paradigm. When AI systems are deployed in social contexts, however, the overall design of such systems is often naive --- a centralized entity provides services to passive agents and reaps the rewards. Such a paradigm need not be the dominant paradigm for information technology. In a broader framing, agents are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems. Agents may supply data and other resources to the system, only if it is in their interest to do so. Critically, intelligence inheres as much in the overall system as it does in individual agents, be they humans or computers. This is a perspective that is familiar in the social sciences, and a key theme in my work is that of bringing economics into contact with foundational issues in computing and data sciences. I'll emphasize some of the mathematical challenges that arise at this tripartite interface.

Apr 14 - Apr 20, 2024

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  Traveling wave solutions of Reaction-Diffusion equation in Biological mathematics

  Hoyoun Kim, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Apr 18, 12:00 - 13:00

  B9 L2 H2 H2

  Traveling wave solutions of reaction-diffusion systems have been studied to explain wave propagation phenomena in biological organisms.
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  Towards Self-explainable Deep Learning Models

  Prof. Michael Kampffmeyer, UiT The Arctic University of Norway

  Apr 16, 16:30 - 17:00

  B1 L4 R 4102

  Despite the significant advancements deep learning models have brought to solving complex problems in the real world, their lack of transparency remains a significant barrier, particularly in deploying them within safety-critical contexts.
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  Can neural ODEs forecast weather?

  Dr. Markus Heinonen, Academy Research Fellow, Aalto Univeristy, Finland

  Apr 16, 16:00 - 16:30

  B1 L4 R 4102

  Neural ODEs have surfaced in the last decade as a new perspective on modelling dynamics by learning the time-derivative that drives the system evolution forward as a neural network.

Mar 31 - Apr 6, 2024

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  A Correlated Three-Dimensional Integral Fractional Ornstein-Uhlenbeck Process Model for Animal Movement

  Jose Hermenegildo Ramirez Gonzalez, Postdoctoral Research Fellow, Statistics

  Apr 4, 12:00 - 13:00

  B9 L2 H2 H2

  Predicting the paths of animals poses a significant challenge, given the intricate nature of their behaviors, the impact of unpredictable environmental elements, individual differences, and the scarcity of precise data on their movements.

Mar 24 - Mar 30, 2024

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  Classification of time series in high dimensions

  Sarbojit Roy, Postdoctoral Research Fellow, Statistics

  Mar 28, 12:00 - 13:00

  B9 L2 H2 H2

  As more and more modern time series data sets are becoming high dimensional, the problem of classification in this context has received increasing attention. We propose a statistical framework for classifying multivariate stationary Gaussian time series where the number of covariates, the length of the series, and the sample size, all grow to infinity.

Mar 17 - Mar 23, 2024

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  Tracking rare events within the ensemble Kalman filtering

  Gaukhar Shaimerdenova, Ph.D. Student, Applied Mathematics and Computational Science

  Mar 21, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF).

Mar 3 - Mar 9, 2024

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  Acoustics with a spin: A new paradigm for acoustic metamaterials, scattering, and propagation of sound

  Mohamed D. Farhat, Research Scientist, Waves in Complex Media

  Mar 7, 12:00 - 13:00

  B9 L2 H2 H2

  When pressure acoustic waves interact with rotating scatterers, they undergo peculiar and intriguing characteristics. In this talk, I will discuss our recent findings on the physics of acoustic scattering and propagation in spinning fluids.