Events

Feb 9 - Feb 15, 2025

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  Bridging Optimal Control and Modern AI Techniques

  Tingwei Meng, Assistant Professor, Computational and Applied Mathematics, University of California,

  Feb 9, 12:00 - 13:00

  Auditorium between 2 and 3

  The convergence of artificial intelligence (AI) and applied mathematics offers transformative potential for addressing challenges in scientific computing and control. In this talk, I will highlight my research at the intersection of optimal control and modern AI methods, focusing on the development of scalable, robust, and efficient algorithms to tackle high-dimensional systems with uncertainty.

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 Sciences

  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.
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  Random Matrix and SYK model

  Prof. Gang Tian

  Feb 4, 16:00 - 17:00

  B1, L3, R3119

  The random matrix theory was started in the 1930s, with notable contributions made by J. Wishart, Baolu Xu and others. During the 1950s of last century, physicist E. Wigner introduced random matrices as a means to simulate the nuclei of heavy atoms, predicting that the spacing between the spectra of heavy atomic nuclei should mirror the spacing between the eigenvalues of random matrices. Many prominent mathematicians also worked on this theory and made very important progress. In this talk, I will begin with some basics on the random matrix theory, then I will discuss some results obtained by my collaborators and myself. Next I will briefly discuss the SYK model, which is now a very active research area in physics. Our objective is to establish a mathematical theory for the SYK model, and I will present some advancements we made as well as some open problems.
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  Metrics, Mayhem, and Microservices: Taming the Cloud Observability Beast

  Alessandro Cornacchia, Postdoctoral Fellow

  Feb 3, 12:00 - 13:00

  B9, L2, R2325

  Cloud applications scale their workload on massively distributed software and hardware infrastructure to deliver swift performance and meet stringent service level objectives. The latest advancements in AI and the promising results delivered by flagship AI models have reinforced this trend, fueling considerable additional investments by all major cloud players to further scale their infrastructure. Yet, fault tolerance and performance debugging remain among the relatively few levers against this exponential growth, demanding ubiquitous system instrumentation and observability to anticipate failures or identify root-causes post-mortem. Overall, observability has become mission-critical to operate cloud technology at scale. In this talk, we introduce the first-ever offloading of observability operations to data centers' Infrastructure Processing Units (IPU) accelerators. This novel architecture can significantly reduce the costs and increase operational efficiency to manage large-scale cloud systems serving millions of users daily.
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  KAUST International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, IMG 2025

  Feb 3 - 5, All day

  Building 19 and 20

  Multigrid Domain Decomposition HPC computational fluid dynamics Computational finance

  The International Conference on Multigrid and Multiscale Methods in Computational Science (IMG) offers a dynamic forum for researchers to share advances in multigrid, multilevel and multiscale computational techniques, as well as their applications across scientific domains.
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  Vehicular Edge Services

  Dr. Hossam Hassanein, Professors, School of Computing, Queen's University

  Feb 2, 12:00 - 13:00

  B9 L2 R2325

  The seminar explores the research of innovative edge computing solutions for Autonomous and Connected Vehicles (AVs and CVs) by leveraging their computational resources, predicting their availability for reliable task offloading, and enhancing task allocation through reputation scoring and quality-aware offloading for improved road safety and traffic efficiency.
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  IEEE Antennas and Propagation Society Distinguished Lecturer Workshop

  Feb 2 - 4, All day

  Auditorium between Buildings 2 and 3

  Workshops

  antenna design Radio Wave Propagation Modeling wave propagation IEEE

  Global and regional experts and leaders gather at KAUST for the IEEE AP-S Distinguished Lecturer Workshop 2025 to share advancements in antennas and propagation research.

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 Sciences

  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.
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  An introduction to Virtual Elements in 3D

  Professor Lourenco Beirao da Veiga, University of Milano-Bicocca

  Jan 28, 16:00 - 17:00

  B1, L3, R3119

  The Virtual Element Method (VEM), is a very recent technology introduced in for the discretization of partial differential equations, that has shared a good success in recent years. The VEM can be interpreted as a generalization of the Finite Element Method that allows to use general polygonal and polyhedral meshes, still keeping the same coding complexity and allowing for arbitrary degree of accuracy. The Virtual Element Method makes use of local functions that are not necessarily polynomials and are defined in an implicit way. Nevertheless, by a wise choice of the degrees of freedom and introducing a novel construction of the associated stiffness matrixes, the VEM avoids the explicit integration of such shape functions.
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  Extreme Temperature Performance and Heterogenous Integration of AlGaN/GaN HEMTs for Power Electronics

  Mritunjay Kumar , Ph.D. Student, Electrical and Computer Engineering

  Jan 28, 15:30 - 17:30

  B2, L5, R5220

  Power integrated circuits (PICs) for extreme temperature applications, such as space exploration and automotive systems, require devices that can operate reliably under harsh conditions.
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  Comments about Research in Computer Science

  Peter Wonka, Professor, Computer Science

  Jan 27, 12:00 - 13:00

  B9, L2, R2325

  In this talk, I will present my personal opinions about various topics in research, for example: What are the goals of research? What is a good research topic? What skills are important for research? What are different styles of research? What are the different types of organizing research? Etc.
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  High Temperature Performance and Heterogenous Integration of AlGaN/GaN HEMTs for Power Electronics

  Mritunjay Kumar , Ph.D. Student, Electrical and Computer Engineering

  Jan 26, 12:00 - 13:00

  B9, L2, R2325

  Power integrated circuits (PICs) for extreme-temperature applications, such as space exploration (e.g., Venus) and advanced automotive systems, require devices capable of reliable operation under harsh conditions.
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  NumPDE Workshop: Numerical Analysis of PDEs

  Jan 26, 08:45 - Jan 28, 15:35

  Sunday 26/01 (morning, 8.45-13.30) - Auditorium between Building 2 and Building 3; Sunday 26/01 (afternoon, from 13.45) - Building 9, Room 2322 (Lecture Hall); Monday 27/01 and Tuesday 28/01 - Auditorium between Building 4 and Building 5

  PDEs numerical analysis

  Explore cutting-edge numerical analysis of PDEs, including Finite Element Methods, Virtual Element Methods, and Isogeometric Analysis, with leading experts.

Jan 19 - Jan 25, 2025

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  Parametric Loudspeaker: From Theory To Applications

  Jun Yang, Distinguished Professor, Chinese Academy of Sciences

  Jan 23, 10:00 - 11:00

  B1 L3 R3119

  This talk highlights a comprehensive overview of research and technical advances related to PALs, covering modeling and simulation computation, measurements, signal processing, beamsteering, and their implementation and application.

Jan 12 - Jan 18, 2025

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  RSS-based Cooperative Localization with Unknown Model Parameters

  Yingquan Li, Ph.D. Student, Electrical and Computer Engineering

  Jan 16, 10:30 - 12:00

  B1, L3, R3119

  The Internet of Things (IoT) has become a popular research direction in 6G era, with wireless sensor networks (WSNs) serving as an important functional infrastructure. WSNs enable key functionalities such as tracking, prediction, and navigation through data collected by pre-deployed sensors. The performance of WSNs is significantly impacted by the accuracy of node localization, as in many applications, the functionality of the system is inherently related to the precise location of nodes. This emphasizes the importance of developing localization techniques.
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  Enriching Your CV, Expanding Networks, and Building a Reputation

  Majid Hassanizadeh, Senior Professor, Stuttgart Center for Simulation Science (SIMTECH), Stuttgart University, Germany

  Jan 13, 16:00 - 17:00

  B1, L3, R3119; Zoom link

  Navigating the academic and professional landscape as an early-career researcher requires not only technical expertise but also strategic self-presentation, networking, and reputation management. This lecture explores three critical areas to help PhD students and postdoctoral researchers elevate their professional profiles and create opportunities for growth.
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  Derivation of cross-diffusion models in population dynamics: dichotomy, time-scales, and fast-reaction

  Cinzia Soresina, Montalcini Tenure-Track Assistant Professor, Department of Mathematics, University of Trento, Italy

  Jan 13, 14:30 - 15:30

  B1, L3, R3119

  In population dynamics, cross-diffusion describes the influence of one species on the diffusion of another. A benchmark problem is the cross-diffusion SKT model, proposed in the context of competing species to account for stable inhomogeneous steady states exhibiting spatial segregation.

Jan 5 - Jan 11, 2025

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  Emerging Vertical GaN Devices on native GaN substrates

  Biplab Sarkar, Associate Professors, Electrical and Computer Engineering, Indian Institute of Technology (IIT) Roorkee

  Jan 7, 09:00 - 10:00

  B3 L5 R5209

  This talk will explore emerging vertical GaN devices on native substrates, including (i) GaN JBS diode, (ii) vertical junctionless GaN Fin-MOSFETs, (iii) vertical GaN (trench) MOSFETs, etc., to address limitations of lateral GaN HEMTs at high voltages, while also discussing device and process challenges and concluding with an examination of Fermi-level pinning effects on the GaN surface.
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  Fundamentals of Compound Semiconductor based power and RF devices

  Biplab Sarkar, Associate Professors, Electrical and Computer Engineering, Indian Institute of Technology (IIT) Roorkee

  Jan 5, 09:00 - 11:00

  B3 L5 R5209

  The significant market investment in semiconductors, particularly in compound semiconductors like GaN and SiC, suggests a future of rapid electronic device innovation, where application-driven advancements challenge conventional assumptions and lead to unexpected breakthroughs, as exemplified by the dominance of Si in very-high-voltage applications despite SiC and GaN's theoretical advantages.

Dec 22 - Dec 28, 2024

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  SDE, BSDE under Nonlinear Expectation and Fully Nonlinear Path-dependent Parabolic PDE

  Shige Peng, Academician of the Chinese Academy of Sciences, Professor of Mathematics, and Chairman of the Research Center for Mathematics and Interdisciplinary Sciences at Shandong University

  Dec 22, 11:00 - 12:00

  B1, L3, R3119

  In this talk we begin with stochastic differential equations (SDE, or FSDE) and backward stochastic differential equations (BSDE) driven by a d-dimensional G-Brownian motion defined on a nonlinear expectation space. The corresponding BSDE is then a path-solution of the corresponding (deterministic) path-dependent fully nonlinear partial differential equation of second order. This, combined with the powerful deep learning approach, provides an important tool for solving numerical solutions of a system of high dimensional partial differential equations of parabolic type.

Dec 8 - Dec 14, 2024

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  From perfect numbers to Egyptian fraction

  Tianxin Cai

  Dec 12, 16:00 - 17:00

  B5, L5, R5209

  In this talk, the author will discuss several classic number theory problems such as perfect numbers, Waring's problem, Fermat's Last Theorem, Hilbert's 8th problem, Egyptian fractions, etc., and raise some new ideas which are the variety of the classic ones, they are the combination of additive and multiplicative equations. Meanwhile, we raise several interesting problems, new applications or conjectures.
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  A Construction of C^r Conforming Finite Element Spaces in Any Dimension

  Jun Hu

  Dec 12, 12:00 - 13:00

  B1, L3, R3119

  This talk proposes a construction of C r conforming finite element spaces with arbitrary r in any dimension. It is shown that if k≥2 d r+1 the space P k of polynomials of degree not greater than k can be taken as the shape function space of C r finite element spaces in d dimensions. This is the first work on constructing such C r conforming finite elements in any dimension in a unified way.
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  Integral models with nonlocal operators: applications and recent development

  Qiang Du, Fu Foundation Professor of Applied Mathematics at Columbia University

  Dec 10, 16:00 - 17:00

  B1, L3, R3119

  Recent applications and theoretical developments of integral models using nonlocal operators have shown promise as effective alternatives to local models, especially in the presence of singularities and anomalies.
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  Innovating Tomorrow: Research, Opportunities, and Future Semiconductor and Digital Technologies

  Sohail Faizan Shaikh

  Dec 10, 14:00 - 15:00

  B3, L5, R5220

  Electronics have become an integral part of our daily lives, thanks to the continuous scaling and maturity of CMOS technology. Over the past decade, we've witnessed the rise of new applications such as health monitoring, point-of-care diagnostics, and environmental monitoring, which often require physically flexible systems and the emergence of flexible electronics field. With the advent of AI and ML, the race to develop hardware that meets these immense demands and challenges has begun. As we push the boundaries of technology, the importance of collaboration cannot be overstated.

Dec 1 - Dec 7, 2024

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  Causal Recommender Systems

  Xiao-Hua (Andrew) Zhou

  Dec 5, 16:00 - 17:00

  B1, L3, R3119

  In this presentation, I will give an overview of the recommender systems (RS) from causal prospective and discuss my team works on the causal recommendation. First, I introduce the potential outcome framework in recommender system. Second, I provide a brief overview for the debiasing strategies. Then I introduce our woks to address the bias in RS from causal perspective. FinallyI discuss some open problem and future direction in recommender systems.
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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.
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  From Pentamodes to Cable Webs to Masonry Structures

  Graeme Milton

  Dec 5, 10:00 - 11:00

  B1, L3, R3119

  Pentamode materials are a class of materials that are useful for guiding stress. In particular, they have been proposed for acoustic cloaking by guiding stress around objects, and have been physically constructed. A key feature of pentamode materials is that each vertex in the material is the junction of 4 double cone elements.
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  Multilevel sampling and goal-oriented adaptive finite elements for random PDEs and advances in randomized quasi-Monte Carlo

  Yang Liu , Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Dec 4, 16:00 - 18:30

  B2, L5, R5209

  This thesis explores numerical methods for uncertainty quantification in systems governed by random partial differential equations (PDEs) and advances in randomized quasi-Monte Carlo (RQMC) techniques.
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  Monotone Operator methods for proving existence of solutions to Dirichlet-type problems

  Melih Ucer, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Dec 4, 14:15 - 15:15

  B1, L4, R4214

  I will discuss several standard techniques for proving existence of solutions to boundary value problems involving second-order elliptic PDE.
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  Neuro-symbolic methods for Semantic Web ontologies

  Robert Hoehndorf, Associate Professor, Computer Science

  Dec 2, 12:00 - 13:00

  B9, L2, R2325

  Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions.
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  Experimental Cybersecurity Evaluation of Distributed Solar Inverters: Vulnerabilities and Impacts on the Australian Grid

  Dr. Ahmed Musleh, Electrical Engineering, University of New South Wales

  Dec 1, 12:00 - 13:00

  B9, L2, R2325

  The increased adoption of solar power globally presents an environmentally sustainable alternative to traditional energy sources. However, integrating solar distributed generation (DG) systems into the power grid introduces complexities that can impact the grid’s reliability and security.

Nov 24 - Nov 30, 2024

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  Mathematics and Sustainability - A Teaching Perspective - Part 2

  Henrik Shahgholian

  Nov 28, 15:00 - 16:00

  B3, L5, R5220

  In this two-part short course, I will present to the participants my recent activities related to teaching mathematics students about sustainability, including introducing and raising awareness of the sustainability topic.
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  LoCoDL: Communication-Efficient Distributed Optimization with Local Training and Compression

  Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

  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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  Economic Fitness: Concepts, Methods and Applications

  Luciano Pietronero

  Nov 27, 12:00 - 13:00

  B2, L5, R5220

  Economic Fitness and Complexity presents a data-driven approach to economic modeling inspired by the science of Complex Systems.
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  Why ML Works: It’s Not Magic, It’s Networked Systems Wizardry!

  Marco Canini, Associate Professor, Computer Science

  Nov 27, 10:00 - 11:30

  B9, L2, R2322

  machine learning computing system

  Today’s machine learning (ML) solutions are achieving extraordinary success across diverse fields, thanks to their ability to learn complex models and deliver impressively accurate results. While the availability of big data is often credited as a driving force, the unsung hero behind ML’s rise is the rapid innovation in computing and software systems that make these breakthroughs possible and accessible.
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  Constraint Maps and their free boundaries

  Henrik Shahgholian

  Nov 26, 16:00 - 17:00

  Building 1, Level 3, Room 3119

  I shall give an introduction to the topic of constraint maps, which sits on the intersection of free boundaries, and harmonic maps. The talk will start at level of graduate and will also smoothly merge current work that has been developed recently.
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  Spatio-Temporal Statistics in Geo-Environmental Data Science

  Ying Sun, Associate 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.
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  Mathematics and Sustainability - A Teaching Perspective - Part 1

  Henrik Shahgholian

  Nov 25, 15:00 - 16:00

  B3, L5, R 5220

  In this two-part short course, I will present to the participants my recent activities related to teaching mathematics students about sustainability, including introducing and raising awareness of the sustainability topic.
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  Data Augmentation is Regularization

  Maurizio Filippone, Associate Professor, Statistics

  Nov 25, 12:00 - 13:00

  B9, L2, R2325

  Data augmentation is a popular technique to considerably improve training of neural networks in various applications, such as computer vision and language modeling.
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  Cost-effective Large-area Digital Circuits via Adhesive Lithography

  Zhanibek Bizak, Ph.D. Student, Electrical and Computer Engineering

  Nov 25, 10:00 - 11:00

  B3, R5, R5220

  A silicon-based semiconductor has had revolutionary impact in all sectors of our lives. With approaching physical limits of further downscaling, there is a growing need for alternative materials and architectures to continue the sustain technological progress of digital circuits. The emerging technologies should tackle various challenges, including fabrication process complexity reduction, compatibility with other technologies, large-area scalability, application focused design flexibility. These challenges were addressed, and alternative solutions been provided in this dissertation work
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  Harnessing Real-World Data for AI-Driven Wireless Communication: Exploring Cutting-Edge Testbeds for Next-Gen Solution

  Ahmed Nasser Ismail, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Nov 24, 12:00 - 13:00

  B9, L2, R2325

  In the quest for next-generation wireless technologies, real-world data collection has become indispensable for driving AI-based advancements, yet it poses significant challenges requiring practical deployment.

Nov 17 - Nov 23, 2024

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  Efficient and Structure-Preserving Numerical Schemes for Kohn-Sham Gradient Flow Models

  Xiuping Wang, Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 20, 09:00 - 10:00

  B4, L5, R5220

  This dissertation presents efficient and structure-preserving numerical schemes for calculating the electronic structure via the Kohn-Sham density functional theory (DFT) and gradient flow models. The efficiency relies on the numerical scheme’s unconditional energy stability, orbital-wise splitting design, and linearity. The structure-preserving property refers to both energy stability and orthonormality-preserving. The corresponding theoretical analysis is rigorously conducted and presented. Various numerical examples are performed to show the schemes’ effectiveness and robustness.
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  Deep Adaptive Sampling for Numerical PDEs

  Prof. Tao Zhou, Chinese Academy of Sciences

  Nov 19, 16:00 - 17:00

  B1, L3, R3119

  We present a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions.
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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.
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  The KAUST Red Sea Research Conference on Frontier Technology

  Nov 18, 08:00 - Nov 19, 17:00

  Auditorium between Buildings 2 and 3

  Experts from academia, industry, and government explore advancements in data networks, cybersecurity, multimodal sensing, wireless technology, and electro-optics.

Jun 30 - Jul 6, 2024

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  Stacked Intelligent Metasurfaces Enabled Joint Signal Processing and Communication in the Wave Domain

  Chau Yuen

  Jul 1, 10:00 - 11:00

  B1 L3 R3119

  In this talk, we will illustrate the application of SIM in multiple-input multiple-output (MIMO) and multi-user MIMO wireless communications.
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  Synthetic Molecular Communication Across Different Scales: From Theory to Experiments

  Robert Schober

  Jun 30, 10:00 - 11:00

  B1 L3 R3119

  In this talk, we will provide a critical analysis of the state of the art in MC research and outline the training and research program of SyMoCADS with the objective of inspiring similar programs elsewhere.

Jun 23 - Jun 29, 2024

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  Mega Constellations - Trends, Technologies and Vision

  Symeon Chatzinotas

  Jun 27, 10:00 - 11:00

  B1 L3 R3119

  In this talk, I will present a vision of future research topics of long-term importance in the area of Satellite Communications and Non-Terrestrial Networks.

Jun 9 - Jun 15, 2024

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  On the Natural Gradient Descent

  Prof. Levon Nurbekyan

  Jun 11, 16:00 - 17:00

  KAUST

  gradient methods

  Abstract Numerous problems in scientific computing can be formulated as optimization problems of suitable parametric models over parameter spaces. Neural network and deep learning methods provide unique capabilities for building and optimizing such models, especially in high-dimensional settings. Nevertheless, neural networks and deep learning techniques are often opaque and resistant to precise control of their mathematical properties in terms of architectures, hyperparameters, etc. Consequently, optimizing neural network models can result in a laborious hyperparameter tuning process that

Jun 2 - Jun 8, 2024

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  Cellular Mesoscale Visualization from Electron Microscopy Imaging

  Ngan Nguyen, Ph.D. Student, Computer Science

  Jun 6, 13:00 - 15:00

  B1 L4 R4214

  Currently, the acquisition of accurate cryogenic electron microscopy data deals with problems with complex and time-consuming processes, low signal-to-noise ratio, and missing wedge, leading to a lack of highly accurate imaging data. Such data would be necessary to develop computational methods/visualizations and essential to train deep learning models that are used to solve inverse problems.