Advice and Comments About Research Peter Wonka, Professor, Computer Science Sep 22, 12:00 - 13:00 B9 L2 R2325 academic life This talk provides a collection of advice and commentary on the multifaceted nature of the research process and life as a graduate student.
From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 15, 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.
Building a Safer and More Equitable Internet in the Age of AI Zafar Qazi, Associate Professor, Computer Science, Lahore University of Management Sciences (LUMS) Sep 8, 12:00 - 13:00 B9 L2 R2325 This talk will show how interdisciplinary collaboration at the intersection of networked systems, AI, and societal impact can help address challenges related to AI-powered moderation systems guiding access to online resources.
Revolutionizing Datacenter Networks via Reconfigurable Topologies Stefan Schmid, Professor, Computer Science, Technische Universität Berlin Sep 1, 12:00 - 13:00 B9 L2 R2325 This talk presents an overview of reconfigurable datacenter networks. In particular, we discuss the motivation for such reconfigurable architectures, review the technological enablers, and present a taxonomy that classifies the design space into two dimensions: static vs. dynamic and demand-oblivious vs. demand-aware.
(Ultra)wide Bandgap Semiconductors for Future of Moore’s Law Xiaohang Li, Associate Professor, Electrical and Computer Engineering Aug 31, 12:00 - 13:00 B9 L3 R2325 (Ultra)wide bandgap compound semiconductors including AlN, GaN, Ga2O3 and In2O3 have attracted enormous interests. They offer markedly larger figures of merits for power and RF applications than other known semiconductors. Additionally, they can be applied for vastly impactful quantum information technologies and deep UV-visible optoelectronics. Moreover, they could be promising for More Moore, More than Moore, and Beyond Moore applications. This seminar will cover the latest material, device and IC research based on (ultra)wide bandgap semiconductors for the future of Moore’s Law.
Visual Analytics for Macromolecular Science Deng Luo, Ph.D. Student, Computer Science Aug 25, 14:00 - 16:00 B1 L2 R2202; Zoom Meeting 96140008642 bioinformatics artificial intelligence nanovisualization This dissertation presents a suite of scientific visual analytics systems that tightly couple large-scale computational modeling - including both AI-powered inference and traditional molecular simulations - with interactive, scalable visualization.
Asymptotic Analysis of Precoding in Large-Scale Multi-User Systems Xiuxiu Ma, Ph.D. Student, Electrical and Computer Engineering Aug 25, 08:30 - 10:00 B1 L3 R3119 MIMO Massive MIMO This thesis develops and applies novel Gaussian Min-Max Theorems to provide a precise asymptotic performance analysis of complex precoders in massive MIMO systems, successfully characterizing system behavior in previously intractable scenarios involving non-linear post-processing operations.
Toward Robust Multimodal Egocentric Video Understanding Merey Ramazanova, Ph.D. Student, Computer Science Jul 28, 17:00 - 17:45 B4 L5 R5209; Zoom Meeting 92591807769 egocentric video Multimodal learning robust modeling This thesis advances egocentric video understanding through multimodal learning, large-scale dataset development, and robust adaptation techniques; it introduces new models, benchmarks, and methods for building scalable, resilient perception systems that operate effectively in real-world, first-person environments.
Hardware Centric Quantized Convolutional Neural Network and Algorithms Li Zhang, Ph.D. Student, Electrical and Computer Engineering Jul 24, 09:00 - 10:00 B3 L5 R5209 machine learning accelerators FPGA This thesis addresses the challenges of deploying quantized convolutional neural networks (QCNNs) on resource-constrained edge devices by proposing two novel hardware-software co-design frameworks: one for deriving lightweight, hardware-friendly models validated on FPGA, and another for hardware-aware mixed-precision quantization on compute-in-memory accelerators.
Simulation of Metasurfaces Described by Generalized Sheet Transition Conditions Using Integral Equations Sebastian Celis Sierra, Ph.D. Student, Electrical and Computer Engineering Jul 23, 10:00 - 11:00 B3 L5 R5209 computational electromagnetics Metasurfaces three-dimensional This dissertation details the development of several novel integral equation solvers that incorporate Generalized Sheet Transition Conditions (GSTCs) to enable the accurate and computationally efficient simulation of complex, multiscale metasurfaces in three-dimensional problems.
Design of Neuromorphic Object Detection Systems Diego Augusto Silva, Ph.D. Student, Electrical and Computer Engineering Jul 16, 16:00 - 17:00 B3 L5 R5209 event-based object detection neuromorphic vision systems efficient deep learning architectures This dissertation advances event-based object detection by developing efficient deep learning frameworks such as ReYOLOv8 and Chimera, introducing novel encoding and augmentation techniques, releasing a new neuromorphic dataset, and demonstrating a real-time, low-power traffic monitoring system that highlights the practical potential of bio-inspired vision systems.
Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations Xiaofeng Xu, Ph.D. Student, Applied Mathematics and Computational Sciences Jul 15, 17:00 - 19:00 B4 L5 R5220 PDEs optimization machine learning randomized orthogonal greedy algorithm This thesis introduces the randomized orthogonal greedy algorithm (ROGA) to bridge the gap between theoretical and practical performance of shallow neural networks for solving partial differential equations by overcoming key optimization challenges to achieve provably optimal convergence rates.
Towards Scalable and Efficient Semantic Video Search Mattia Soldan, Ph.D. Student, Electrical and Computer Engineering Jul 13, 18:00 - 19:00 B4 L5 R5209 video-language grounding semantic video retrieval multimodal alignment This dissertation advances fine-grained, content-aware video retrieval by developing novel models and frameworks for Video-Language Grounding, enabling accurate alignment between natural language queries and specific temporal segments in unstructured video content.
Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems Juan Pablo Muñoz Díaz, Ph.D. Student, Applied Mathematics and Computational Sciences Jul 9, 09:00 - 10:00 B2 L5 R5220; Zoom Meeting 95274807609 generative ai applied mathematics bioscience Dynamical Systems deep learning This thesis presents a data-driven framework for discovering low-dimensional generative models of complex systems by using a library of normal-form equations to identify both observable dynamics and hidden control variables directly from time-series data.
Theory and Implementation of Novel Numerical Methods for Multiphysics Interface Problems Najwa Alshehri, Ph.D. Student, Applied Mathematics and Computational Sciences Jul 8, 15:00 - 17:00 B2/B3 L0 R0215 finite element method numerical analysis fluid-structure interactions mixed finite elements This thesis develops and analyzes Finite Element Methods (FEM) for multiphysics interface problems using the Fictitious Domain with Distributed Lagrange Multiplier (FD-DLM) framework. It introduces new families of stable mixed methods with discontinuous Lagrange multiplier spaces, studies both a priori and a posteriori error estimates, and designs multigrid preconditioners. Theoretical results are supported by numerical experiments.
Towards Usable and Useful Explainable AI Lijie Hu, Ph.D. Student, Computer Science Jul 7, 17:00 - 19:00 B3 L5 R5220 explainable AI Large Language Models multimodal models This talk presents advancements in Explainable AI, spanning from classical deep learning to large language models, with contributions that enhance both the usability and usefulness of interpretability methods to improve trust, performance, and safety in AI systems.
Explainability and Efficiency in Spatio-Temporal Models: Applications to Traffic Forecasting Xiaochuan Gou, Ph.D. Student, Computer Science Jul 6, 15:00 - 18:00 B5 L5 R5209 traffic forecasting Graph Neural Networks model interpretability This dissertation addresses key challenges in deep learning-based traffic forecasting, including computational efficiency, model interpretability, and data limitations, despite recent progress in spatio-temporal modeling techniques.
Modern Privacy-preserving Machine Learning: Rigorous Approach for Data Privacy Zihang Xiang, Ph.D. Student, Computer Science Jul 6, 10:00 - 12:00 B3 L5 R5216 privacy-preserving machine learning Differential privacy Federated learning This dissertation centers around privacy-preserving technologies (differential privacy) in broad machine learning applications. This dissertation focuses on two sides of differential privacy: 1) designing privacy-preserving algorithms, 2) ensuring the falsifiability of privacy claims.
Towards Efficient AI Hardware: Software-Hardware Co-Design for In-Memory Computing Accelerators Olga Krestinskaya, Ph.D. Student, Electrical and Computer Engineering Jun 29, 08:00 - 10:00 Zoom Meeting 980 9796 2723; B4 L5 R5220 This dissertation addresses two key challenges in software–hardware co-design for IMC-based neural network accelerators: (1) the need to develop generalized IMC hardware that can efficiently support multiple neural network models, and (2) the need for automated frameworks that jointly optimize model parameters, quantization schemes, and IMC hardware configurations for workload-specific deployments.
Computer Vision for Video Editing Learning to Cut, Classify, Assemble, and Generate Alejandro Pardo, Ph.D. Student, Electrical and Computer Engineering Jun 23, 10:00 - 12:00 Click here to join the Ph.D. defense via Zoom This thesis advances video editing by developing a suite of computer vision models for understanding and generating editorial decisions, including a method for ranking video cuts, a dataset for classifying cut types, a language-guided timeline assembler, and a diffusion-based technique for creating match cuts.
Developing Novel Fabrication Techniques and Device Modeling for High-Efficient III-Nitride Micro-Sized Light Emitting Diodes Zhiyuan Liu, Ph.D. Student, Electrical and Computer Engineering Jun 3, 10:00 - 12:00 B2 L5 R5209 This thesis addresses current challenges in InGaN and AlGaN micro-LEDs, including efficiency degradation due to sidewall damage and the high complexity of the micro-LED fabrication process.
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 Many Faces of Compression: Theory and Practice in Federated Optimization Elnur Gasanov, Ph.D. Student, Computer Science May 28, 10:00 - 12:00 B3, L5, R5209 compression methods communication efficiency proximal algorithms This thesis presents novel compression methods and optimization algorithms aimed at improving communication efficiency in large-scale machine learning systems.
Bridging AI and Spatial Biology to Navigate Biological Landscapes Haoyang Li, Ph.D. Student, Computer Science May 20, 11:00 - 13:00 B3 L5 R5209; Zoom Meeting 2343423945 AI artificial intelligence Computational biology bioinformatics spatial transcriptomics This thesis presents several studies by proposing AI-driven methods to supply a comprehensive and precise representation of biological landscapes via spatially resolved transcriptomics (SRT) data.
Towards Privacy-preserving Artificial Intelligence (PAI) for Healthcare and Bioinformatics Juexiao Zhou, Ph.D. Student, Computer Science May 20, 09:00 - 11:00 B3, L5, Room 5209 privacy-preserving AI Federated learning healthcare applications This thesis presents innovative privacy-preserving AI solutions for healthcare and bioinformatics, including federated learning for multi-omics, data deletion from deep learning models, and on-device medical analysis with vision LLMs, advancing secure and ethical AI development.
Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025 May 18, 09:00 - May 25, 15:30 Auditorium 0215 between B2 & B3 stochastic algorithm statistical learning machine learning Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.
Perturbation Methods in PDE: from Heuristics to Breakthroughs in Regularity Theory - Part 3 Dr. Eduardo Teixeira, Professor of Mathematics, Department of Mathematics, University of Central Florida, USA May 15, 16:15 - 17:30 B1 L4 R4102 This mini-course presents a powerful perturbative framework tailored to tackle critical regularity issues in nonlinear diffusion PDE.
FMCW Radar Applications for Automotive and Biomedical Applications Vijith Varma Kotte, Ph.D. Student, Electrical and Computer Engineering May 15, 15:00 - 17:00 B1 L3 R3119 deep learning Signal processing MIMO radars sensors This dissertation enhances Frequency Modulated Continuous Wave (FMCW) radar effectiveness for automotive forward-looking Synthetic Aperture Radar (SAR) imaging and biomedical vital sign and dehydration monitoring by developing novel methodologies based on advanced signal processing, deep learning, and MIMO radar techniques.
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.
Data–Driven Mining of Causal Disease Relations to Enhance Disease Centric Predictions Sumyyah Toonsi, Ph.D. Student, Computer Science May 15, 10:00 - 11:00 B4 L5 R5209 This thesis develops a framework to extract and leverage inter-disease causal relations from biomedical literature, thereby advancing disease-centric predictions, enhancing our understanding of disease mechanisms, and demonstrating the potential for causal knowledge-guided therapeutic discovery.
Perturbation Methods in PDE: from Heuristics to Breakthroughs in Regularity Theory - Part 2 Dr. Eduardo Teixeira, Professor of Mathematics, Department of Mathematics, University of Central Florida, USA May 14, 16:15 - 17:30 B1 L3 R3119 This mini-course presents a powerful perturbative framework tailored to tackle critical regularity issues in nonlinear diffusion PDE.
Signal Alignment: a Practical Way to Communicate Under Unpredictable Interference Nikolaos Sidiropoulos, Louis T. Rader Professor Electrical and Computer Engineering, University of Virginia, USA May 14, 13:30 - 14:30 B1 L3 R3119 Wireless communication This talk introduces a practical method for reliable wireless communication amidst interference by using packet repetition and multi-antenna reception to achieve signal alignment, enabling packet recovery through geometric subspace intersection even against adversarial jammers.
The Internet of Fiber-Optic Things and Smart Sensing Juan Manuel Marin Mosquera, Ph.D Candidate (former), Electrical and Computer Engineering May 14, 10:00 - 11:30 B1 L3 R3119 This dissertation introduces the Internet of Fiber-Optic Things (IoFOT)—a new concept where a single optical fiber handles data, power, and smart sensing simultaneously. Demonstrated applications include pipeline monitoring and marine life tracking, paving the way for the development of a worldwide smart observation network.
Nonlocal Degenerate PDEs: Bridging Free Boundary Problems and Critical-point Models Eduardo Teixeira, Graduate Director & Professor, Department of Mathematics, University of Central Florida May 13, 16:00 - 17:00 Building 1, Level 3, Room 3426 nonlocal degeneracies local extrema models regularity estimates This talk explores a new class of PDEs featuring nonlocal degeneracies. Our framework unifies two classical scenarios — free boundary problems and critical-point degenerate PDEs; both are recast as local extrema models in our formulation.
Error Feedback for Communication-Efficient First and Second-Order Distributed Optimization: Theory and Practical Implementation Konstantin Burlachenko, Ph.D. Student, Computer Science May 12, 12:00 - 13:00 B9 L2 R2325 Federated learning software development This seminar will discuss advancements in Federated Learning, including theoretical improvements to the Error Feedback method (EF21) for communication-efficient distributed training and the development of significantly more practical and efficient implementations of the Federated Newton Learn (FedNL) algorithm.
Topological Phenomena in Artificial Materials Xiujuan Zhang, Associate Professor, School of Engineering and Applied Sciences, Department of Material Science and Engineering, Nanjing University May 12, 10:00 - 11:00 B1 L3 R3119 topological phenomena artificial materials This talk will cover the design and realization of novel topological states and effects in acoustic artificial materials, focusing on higher-order topological states, non-Hermitian physics, and acoustic spin- and orbital angular momentum-related topological phenomena.
Xiujuan Zhang, Associate Professor, School of Engineering and Applied Sciences, Department of Material Science and Engineering, Nanjing University
Carrier- and Trap-Resolved Photo-Hall Effect: Unlocking the 145-Year-Old Secret in Hall Effect Oki Gunawan, Research Staff Member, IBM Research, USA May 12, 10:00 - 11:00 B5 L5 R5209 advanced semiconductors This seminar introduces the "carrier and trap resolved photo-Hall effect," a novel technique extending the classic Hall effect via a simple hyperbola equation to reveal majority/minority carrier properties and trap dynamics, unifying multiple physical excitations for enhanced semiconductor characterization.
Perturbation Methods in PDE: from Heuristics to Breakthroughs in Regularity Theory - Part 1 Dr. Eduardo Teixeira, Professor of Mathematics, Department of Mathematics, University of Central Florida, USA May 11, 16:15 - 17:30 B1 L3 R3119 This mini-course presents a powerful perturbative framework tailored to tackle critical regularity issues in nonlinear diffusion PDE.
Structuring Sound and Vibration by Metasurfaces Badreddine Assouar, Professor, Director of Research at French National Center of Scientific Research (CNRS), University of Lorraine, France May 11, 12:00 - 13:00 B9 L2 R2325 This seminar provides an overview of recent research on acoustic and elastic metasurfaces and metamaterials, covering their fundamentals, applications in wave and vibration control, including low-frequency absorption, BIC physics, and phonic skyrmions.
Optimization Methods and Software for Federated Learning Konstantin Burlachenko, Ph.D. Student, Computer Science May 8, 19:00 - 21:00 B5 L5 R5209 This dissertation identifies five key challenges in Federated Learning (FL), including data and device heterogeneity, communication issues, privacy concerns, and software implementations. More broadly, our work serves as a guide for researchers navigating the complexities of translating theoretical methods into efficient real-world implementations, while also offering insights into the reverse process of adapting practical implementation aspects back into theoretical algorithm design.
Spatiotemporal Machine Learning for Real-world Complex Systems: Enhancing Smart Transportation with Robust AI ZIyue Li, Assistant Professor, Information Systems Department, University of Cologne, Germany May 8, 13:00 - 14:00 B1 L4 R4214 This seminar introduces advanced machine learning approaches, including tensor methods for modeling high-dimensional mobility data and deep learning techniques designed to handle data corruptions, aiming to improve the reliability of spatiotemporal analytics for smarter transportation systems.
Modeling and Simulation of Carbon Dioxide Storage in Geological Layers Shuai Lu, Ph.D. Student, Computer Science May 8, 12:00 - 14:00 B2 L5 R5220 This dissertation advances large-scale simulation of carbon dioxide storage by developing a novel mathematical model and numerical schemes, which are validated by benchmark and real-world cases.
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.
High-Mobility Back-End-of-Line Compatible Indium Oxide Thin-Film Transistors for Monolithic 3D Integration Na Xiao, Ph.D. Student, Electrical and Computer Engineering May 6, 16:00 - 18:00 B2 L5 R5209 advanced semiconductors thin-film transistors This dissertation develops strategies for fabricating high-performance, low-temperature processed indium oxide thin-film transistors for monolithic 3D integration, achieving record mobility and stability through optimized annealing, passivation, and channel engineering techniques.
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.
The 2025 Iberian Blackout: Anatomy of a Grid Collapse Charalambos (Harrys) Konstantinou, Associate Professor, Electrical and Computer Engineering May 6, 09:00 - 11:00 B9 L2 R2322 renewable energy resilience power grid This talk provides a data-informed timeline of the April 28 Iberian blackout, examining how system conditions and possible instabilities may have shaped the cascade of events.
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.
First-Order Mean-Field Games with Entry-Exit Flow Constraints and Contact-Set Conditions AbdulRahman Alharbi, Ph.D. Student, Applied Mathematics and Computational Sciences May 4, 12:30 - 14:30 B9 L4 R4225; Zoom Meeting 95071981979 mean-field models free boundary problems This dissertation develops and rigorously analyzes first-order mean-field game models incorporating novel mixed boundary conditions to realistically represent population dynamics in bounded domains by eliminating unrealistic entry phenomena that are typically induced by standard Dirichlet conditions.
AI for Chips and Chips for AI Mehdi Saligane, Assistant Professor, Electrical and Computer Engineering, Brown University, USA May 4, 12:00 - 13:00 B9 L2 R2325 artificial intelligence AI semiconductors innovation LLM This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.
