Events

Dec 8 - Dec 15, 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.

Dec 1 - Dec 8, 2024

• 

  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.
• 

  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.

Nov 24 - Dec 8, 2024

• 

  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 - Dec 1, 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.
• 

  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.
• 

  Economic Fitness: Concepts, Methods and Applications

  Luciano Pietronero

  Nov 27, 12:00 - 13:00

  B2, L5, R5220

  Economic Fitness and Complexity (EFC) is the recent economic discipline and methodology we have developed in the past ten years. EFC makes use and develops the modern techniques of data analysis to build economic models based on a scientific methodology inspired by the science of Complex Systems with special attention to quantitative tests to provide a sound scientific framework.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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

Nov 17 - Dec 1, 2024

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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 24, 2024

• 

  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.
• 

  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.
• 

  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.
• 

  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 7, 2024

• 

  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.

Jun 23 - Jul 7, 2024

• 

  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 30, 2024

• 

  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 16, 2024

• 

  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 9, 2024

• 

  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.
• 

  Unlocking Spectrum Synergy: Seamless Radar-Communication Integration by Leveraging MIMO Beampattern Invariance

  Sana Mazahir, Ph.D. Student, Electrical and Computer Engineering

  Jun 4, 11:00 - 13:00

  B1 L3 R3119

  The Dual-function radar communication (DFRC) refers to an integrated system that performs both the functions of a radar and a communication system. It is designed by exploiting the tractability and reusability of both radar and communication systems' components, parameters, and spectrum to achieve an integrated system. This dissertation explores and exploits the flexibility in the transmit beampattern design in MIMO radar systems to implement the transmission of communication symbols.
• 

  Integration is all you need: a vision of joint sensing, learning, and communication networks

  Nuria Gonzalez Prelcic, Professor, Electrical and Computer Engineering, University of California

  Jun 4, 10:00 - 11:00

  B1 L3 R3119

  I will address the concept of multimodal integration, where information from conventional sensors and network sensing modalities can be fused for different purposes.

May 26 - Jun 2, 2024

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  ML-based Network Intrusion Detection Systems: Resilience threats and a mitigation approach

  António Casimiro is an Associate Professor at the Department of Informatics of the University of Lisboa Faculty of Sciences (FCUL)

  May 30, 15:30 - 16:30

  B4 L5 R5220

  cybersecurity cyber threats

  With the ever-increasing amount of cyberthreats out there, securing IT and OT infrastructures against these threats has become not only desirable, but fundamental. Network Intrusion Detection Systems (NIDS) are key assets for system protection, providing early alerts of network attacks. An important class of NIDS are those based on ML techniques, around which a substantial amount of research is being done these days. Unfortunately, being ML-based, these NIDS can be targeted by adversarial evasion attacks (AEA), which malicious parties try to exploit to perform network attacks without being detected.
• 

  Stein Variational Gradient Descent and Consensus-Based Optimization: Towards a Convergence Analysis and Generalization

  Lukang Sun, Ph.D. Student, Computer Science

  May 30, 11:00 - 14:00

  B3 L5 R5220

  The first part of the dissertation presents a study on the convergence properties of Stein Variational Gradient Descent (SVGD), a sampling algorithm with applications in machine learning. The research delves into the theoretical analysis of SVGD in the population limit, focusing on its behavior under various conditions, including the Talagrand’s inequality T1 and the (L0, L1)−smoothness condition. The study also introduces an improved version of SVGD with importance weights, demonstrating its potential to accelerate convergence and enhance stability.
• 

  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.
• 

  A Whole-genome Model of DNA Replication in Human Cells

  Dr. Francisco Berkemeier, University of Cambridge

  May 28, 16:00 - 17:00

  B5 L5 R5209

  genome biological models

  DNA replication is a fundamental cellular process and its precise regulation is crucial for maintaining genomic integrity. Inspired by the mathematical parallelism between DNA replication and the nucleation problem in one-dimensional crystal growth kinetics, we introduce a model that maps whole-genome replication dynamics based on the firing rate profiles of replication origins and fork movement.
• 

  Compressed Communication in Distributed Deep Learning and Generative Language Models

  Hang Xu, Ph.D. Student, Computer Science

  May 28, 15:00 - 17:00

  B2 L5 R5209

  Deep Learning and generative Artificial Intelligence has grown rapidly during the past few years due to the advancement of computing powers and parallel distributed training algorithms. As a result, it has been a common practice to use hundreds or thousands of machines to train very large Deep Neural Networks.

May 19 - May 26, 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.
• 

  Advancing Plasmonic Modulation: CMOS-Compatible MIS-Schottky Devices for Integrated Photonics

  Dr.Nasir Alfaraj, Department of Electrical and computer Engineering, University of Toronto

  May 21, 10:00 - 12:00

  B9 L3 R3128

  Recent developments in silicon-integrated plasmonics offer immense potential for miniaturized photonic circuits. In this work, we demonstrate a CMOS-compatible metal-insulator-semiconductor (MIS) plasmonic modulator based on a Schottky heterojunction. Our device integrates amorphous aluminum, silica, and indium tin oxide on a silicon-on-insulator (SOI) substrate.

May 12 - May 26, 2024

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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

  Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

  May 19, 08:00 - May 30, 17:00

  B2 B3 A0215

  stochastic algorithms statistical learning machine learning

  We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

May 12 - May 19, 2024

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  Hierarchical ensemble-based data assimilation and rare event tracking

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

  May 15, 10:00 - 12:00

  B4 L5 R5220

  numerical analysis

  This thesis consists of three papers considering, in general, two challenges: the minimization of computational cost in the ensemble Kalman filtering (EnKF) method and the problem of tracking a rare event within the framework of the EnKF.
• 

  Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

  Rolf Krause, Professor, Applied Mathematics and Computational Sciences

  May 14, 16:00 - 17:00

  B3 L5 R5209

  machine learning medicine AI

  Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems
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  Towards trustworthy use of scientific machine-learning in large scale numerical simulations

  Alena Kopanicakova, Researcher, Brown University

  May 13, 09:00 - 10:00

  B9 L3 R3128

  numeric simulations

  Recently, scientific machine learning (SciML) has expanded the capabilities of traditional numerical approaches by simplifying computational modeling and providing cost-effective surrogates. However, SciML models suffer from the absence of explicit error control, a computationally intensive training phase, and a lack of reliability in practice. In this talk, we will take the first steps toward addressing these challenges by exploring two different research directions.

May 5 - May 12, 2024

• 

  Intracellular "in silico microscopes" - Fully 3D Spatio-Temporal Virus Replication Model Simulations

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  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.
• 

  Magnetically Controlled Printed Phase Shifters and Reconfigurable Beam Scanning Arrays

  Ulan Myrzakhan, Ph.D. Student, Electrical and Computer Engineering

  May 8, 15:00 - 17:00

  B4 L5 5220

  Array antennas with reconfigurable frequency and polarization, as well as beam-steering capabilities, have become essential for modern wireless systems. Beyond potential cost and space savings, these versatile antennas are expected to enhance both the performance and the security of wireless communication. Traditional designs rely on a large number of active elements for this purpose, resulting in an expensive solution that also leads to complex feeding and biasing networks. Alternatively, reconfigurable operation in microwaves can be achieved through magnetic tuning of ferrite substrates, eliminating the need for active components. Further cost savings can be achieved if additive manufacturing is adopted. These two approaches will be utilized in this dissertation to develop a cost-effective and structurally simple phased array antenna with the desired level of versatility.
• 

  The impact of electrolyte swelling on the mixed conduction properties of n-type organic semiconductors

  Jokubas Surgailis, Ph.D. Student, Electrical and Computer Engineering

  May 8, 11:00 - 13:00

  B5 L5 R5220

  Organic mixed ion-electron conductors (OMIECs) have found themselves in the spotlight of the bioelectronics field because of their potential to bridge the gap between the worlds of biology and electronics. From the initial discovery of conductive polymers just a few decades prior, the evolution of OMIECs has been growing exponentially.
• 

  Optimization for Deep Learning

  Dr. Konstantin Mischenko, Samsung AI Center, Cambridge, UK

  May 7, 16:00 - 17:00

  B5 L5 R5209

  Deep learning mathematical optimization

  Abstract The field of optimization for machine learning has undergone significant changes in recent years with deep learning models increasing in scale and fine-tuning taking a more prominent role. In this presentation, I will share a perspective on the direction of changes in the field and highlight interesting research directions. I will provide real-world examples of what practitioners want from optimization methods to train deep networks at scale. I will then present my recent work on adaptive methods, such as Adam and Adagrad, and explain how we can estimate the learning rate for these
• 

  3D Smart Antenna-in-Package Designs with Orientation Awareness and Self-Reconfigurability for Internet of Things (IoT) Applications

  Maria Bermudez Arboleda, Ph.D. Student, Electrical and Computer Engineering

  May 6, 14:30 - 16:30

  B2 L5 R5220

  IoT devices at the edge of the network are energy-constrained, and a significant portion of power is wasted on non-essential radiation when large coverage antennas are implemented. Additionally, continuous and uncontrolled electromagnetic (EM) radiation contributes to ambient EM pollution. When combined with the projected growth of IoT devices, this raises the chances of interference between devices, leading to potential information loss in dynamic scenarios.

Apr 28 - May 12, 2024

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  Toward long-form video understanding

  Chen Zhao, Research Scientist, Electrical and Computer Engineering

  May 5, 12:00 - 13:00

  B9 L2 H2

  Nowadays, videos are omnipresent in our daily lives. From TikTok clips to Bilibili videos, from surveillance footage to vlogs recordings, the sheer volume of video content is staggering. Processing and analyzing the substantial volume of video data demands immense human effort. While computer vision techniques have made remarkable progress in automating video understanding in short clips, their effectiveness and efficiency when applied to long-form videos still fall short of the mark.
• 

  Multigrid Monte Carlo Revisited: Theory and Bayesian Inference

  Professor Robert Scheichl, University of Heidelberg

  May 5, 11:00 - 12:00

  B1 L3 R3119

  Multigrid Monte Carlo

  Abstract The fast simulation of Gaussian random fields plays a pivotal role in many research areas such as Uncertainty Quantification, data science and spatial statistics. In theory, it is a well understood and solved problem, but in practice the efficiency and performance of traditional sampling procedures degenerates quickly when the random field is discretised on a grid with spatial resolution going to zero. Most existing algorithms, such as Cholesky factorisation samplers, do not scale well on large-scale parallel computers. On the other hand, stationary, iterative approaches such as the
• 

  Averaging, Momentum, and Schedulers in Optimization for Deep Learning

  Konstantin Mishchenko

  May 5, 11:00 - 13:00

  B9 L3 R3128

  Deep learning artificial intelligence

  In this talk, I will present some work in progress on practical optimization methods for deep learning. We will start with a discussion of several empirical techniques that enable training of large-scale models in language and vision tasks, including weight decay, averaging, and schedulers. We will then look at a new approach that we call schedule-free due to its ability to work without a pre-defined time horizon. I will share some details about the theory for these methods, explain why they might be useful in practice and then shed some light on their limitations. This talk will be oriented towards people who already have some knowledge of optimization methods.

Apr 28 - May 5, 2024

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  Non-standard Finite Elements for Eigenvalue Problems

  Linda Alzaben, Ph.D. Student, Applied Mathematics and Computational Sciences

  May 2, 14:00 - 15:00

  B1 L2 R2202

  Numerical approximation of partial differential equations involves parameter dependencies from problem formulation and numerical methods. We focus on two areas: least-squares finite element method with linear elasticity, studying its dependence on the Lamé parameter, and the Virtual Element Method, known for handling complex geometries where the stabilization parameter is analyzed.
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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, Ph.D. (former), 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.
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  Azimuthal Super-Resolution Imaging of Ferromagnetic Tubulars: Deep Neural Network-Based Electromagnetic Inversion

  Guang An Ooi, Ph.D. Student, Electrical and Computer Engineering

  May 2, 09:00 - 11:30

  B4 L5 R5209

  The structural integrity of wellbore casings and transportation pipelines is a critical aspect in the oil and gas industry for operational efficiency and environmental safety. Traditional non-destructive testing methods, while effective, face significant challenges in accurately assessing and monitoring these crucial infrastructural components, especially under harsh operating environments. Furthermore, the inner volume of these tubular structures and the flow speed of transported liquids pose additional difficulties upon the performance of devices designed to inspect their structures for defects and deformations.
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  Virtual Element Approximation of Eigenvalue Problems

  Prof. Francesca Gardini, Università di Pavia

  Apr 30, 16:00 - 17:00

  B1 L3 R3119

  Virtual Element Method Elgenvalue FEMSimulation PDE

  We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.
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  Towards accelerating catalyst discovery: bridging the gap between modeling, experiments, and large-scale integration

  Dr. Jehad Abed, Postdoctoral Researcher, Fundamental AI Research at Meta

  Apr 30, 11:00 - 12:00

  B1 L3 R3426

  In this talk, I will discuss our progress in advancing the discovery of catalysts for green hydrogen production and carbon dioxide conversion, as well as designing novel metalorganic frameworks for direct air capture.