Events

Oct 23 - Oct 29, 2022

• 

  Mean-Field Games on Networks and Wardrop Equilibria - Graduate Seminar

  Ricardo De Lima Ribeiro, Research Specialist, CEMSE, KAUST

  Oct 25, 12:00 - 13:00

  B9 L2 R2322

  mean-field games

  Models for flows on networks arise in the study of traffic and pedestrian crowds. These models encode congestion effects, the behavior and preferences of agents, such as aversion to crowds and their attempts to minimize travel time. We will present the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field game.
• 

  Applied AI for sustainable development

  Prof.Evgeny Burnaev, Applied AI Center, Skolkovo Institute of Science and Technology

  Oct 24, 12:00 - 13:00

  B9 L2 R2322 H1

  Skoltech machine learning Bayesian modeling artificial intelligence

  Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.
• 

  BioChips: Leveraging Integrated Electronics for Sensing, Medicine, and Science

  Pamela Abshire, Professor, Department of Electrical and Computer Engineering and Institute for Systems Research at the University of Maryland, College Park

  Oct 23, 12:00 - 13:00

  B9 L2 R2322

  biomedical electronics Biosensors

  This talk will provide a brief overview of LoCMOS systems, the technologies used to construct them, and their application to novel applications in biosensing, medical diagnostics, and neuroscience. The integration of integrated circuits into LoCMOS devices poses a number of distinct and vexing challenges, increasing complexity while reducing the need for external instrumentation.

Oct 16 - Oct 22, 2022

• 

  Inference for Longitudinal Data After Adaptive Sampling

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 20, 15:00 - 16:00

  B9 L2 H2

  Reinforcement Learning

  In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
• 

  We used Reinforcement Learning; but did it work?

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 19, 16:00 - 17:00

  B9 L2 H2

  Reinforcement Learning Digital Behavioral Health

  Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
• 

  Gradient Estimates for weak solutions of Elliptic PDE's - Colloquium

  Giuseppe Di Fazio, Professor, University of Catania

  Oct 18, 15:30 - 17:00

  B1 L3 R3119

  Elliptic PDE

  In the Seminar, we exploit what is the heart of the technique to show gradient estimates allowing the function ? to belong to very general function spaces. Our technique is very flexible and allows us to show the existence, uniqueness, and well-posedness of the Dirichlet problem in several classes.
• 

  Distributed multi-GPU Algorithms for Hierarchical Matrices

  George Turkiyyah, Research Professor, Applied Mathematics and Computational Science

  Oct 18, 12:00 - 13:00

  B9 L2 R2322

  GPU Algorithms hierarchical matrices

  In this talk, we show that, besides their optimal O(N) algorithmic complexity, hierarchical matrix operations also benefit from parallel scalability on distributed machines with extremely large core counts. In particular, we describe high-performance, distributed-memory, GPU-accelerated algorithms for matrix-vector multiplication and other operations on hierarchical matrices in the H^2 format.
• 

  Recent advances in mixed finite element approximation for poroelasticity

  Arbaz Khan, Assistant Professor, Department of Mathematics, Indian Institute of Technology (IIT)

  Oct 18, 11:00 - 12:00

  B1 L3 R3119

  parallel computing Numerical Solution of Partial Differential Equations stochastic differential equations

  This talk discusses the non-conforming approximation of Biot's consolidation model. In the first part of the talk, we discuss posteriori error estimators for locking-free mixed finite element approximation of Biot’s consolidation model. In the second part of the talk, we discuss a novel locking-free stochastic Galerkin mixed finite element method for the Biot consolidation model with uncertain Young’s modulus and hydraulic conductivity field.

Oct 9 - Oct 15, 2022

• 

  A Fast Normalized Cut with Automated Constraints via Graph Coarsening

  Prof. Young Ju Lee, Department of Mathematics, Texas State University

  Oct 11, 15:30 - 17:00

  B1 L3 R3119

  Normalized Cut Graph Coarsening

  We present a new constrained K-way data clustering algorithm based on Normalized Cut by Shi and Malik. A novelty in our algorithm lies in selecting constraints automatically from the data by using a multiscale coarsening algorithm.
• 

  Finite element approximation of parameter dependent eigenvalue problems

  Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering

  Oct 11, 12:00 - 13:00

  B9 L2 R2322

  Finite element approximation of parameter dependent eigenvalue problems

  Eigenvalue problems arising from partial differential equations are used to model several applications in science and engineering, ranging from vibrations of structures, industrial microwaves, photonic crystals, and waveguides, to particle accelerators.
• 

  EF-BV: distributed optimization with compressed communication

  Laurent Condat, Senior Research Scientist, Computer Science

  Oct 10, 12:00 - 13:00

  B9 L2 R2322 H1

  big data distributed computing Federated learning

  In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.
• 

  Scalable Millimeter-wave Phased Arrays: Challenges and Solutions

  Duixian Liu, Research Staff, IBM T. J. Watson Research Center, New York, USA

  Oct 9, 12:00 - 13:00

  B9 L2 R2322 H1

  Scalable Millimeter

  Advances in Si-based millimeter-wave circuit design, Si-based phased arrays, and low-cost antenna integration techniques have enabled the development of scalable phased arrays supporting 10s to 100s of elements.
• 

  Sensor-less Energy-free Sensing: The Future of Ubiquitous Context-Awareness for the IoT

  Moustafa Youssef, The American University in Cairo, Egypt

  Oct 9, 11:00 - 12:00

  B1 L3 R3119

  mobile wireless networks mobile computing location determination technologies pervasive computing

  Many IoT devices are expected to be limited in capability and run with minimal power sources/limited batteries. To extend their lifetime, and autonomy, and reduce the cost of deployment, we introduce the concepts of sensor-less and energy-free sensing, where we sense the environment without using any external sensors while consuming minimal or no energy.

Oct 2 - Oct 8, 2022

• 

  Towards Structured Intelligence with Deep Graph Neural Networks

  Guohao Li, Ph.D., Computer Science

  Oct 5, 18:00 - 20:00

  B5 L5 R5209

  graph machine learning Graph Neural Networks

  This dissertation discusses approaches to building large-scale and efficient graph machine learning models for learning structured representation with applications to engineering and sciences. This work would present how to make Graph Neural Networks (GNNs) go deep by introducing architectural designs and how to automatically search GNN architectures by novel neural architecture search algorithms.
• 

  Geometry and Computational Design of Equilibrium Structures

  Oct 4, 15:30 - 17:00

  B1 L3 R3119

  Geometry computational design

  Freeform structures play a prominent role in contemporary architecture. In order to stay within reasonable cost limits, computational shape design has to incorporate aspects of structural analysis and fabrication constraints. The talk discusses solutions to important problems in this area. They concern the design of polyhedral surfaces with nearly rectangular faces, polyhedral surfaces in static equilibrium, the smoothest visual appearance of polyhedral surfaces and the closely related problem of finding material-minimizing forms and structures. From a methodology perspective, there is an interplay of geometry, mechanics and optimization. Classical subjects such as isotropic geometry, a simple Cayley-Klein geometry, play a role as well as most recent developments in discrete differential geometry. We also show how practical requirements have led to new results and open problems in geometry.
• 

  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  Oct 4, 12:00 - 13:00

  B9 L2 R2322

  Dynamic programming combinatorial optimization data mining

  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.
• 

  A new class of random field models for data on networks

  David Bolin, Professor, Statistics

  Oct 3, 12:00 - 13:00

  B9 L2 R2322 H1

  applied statistics machine learning Gaussian processes

  Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.
• 

  Demonstration of “WBG and Beyond” Electronics: Towards Large-scale 3D-vertical heterogenous integration

  Oct 2, 12:00 - 13:00

  B9 L2 R2322 H1

  With the continuous reduction of chip feature size, the continuation of Moore's Law becomes increasingly difficult and heterogeneous integration has become one of the important orientations of electronic technology.

Sep 25 - Oct 1, 2022

• 

  Modeling and Numerics of Sorption Kinetics

  Giovanni Russo, Professor, Department of Mathematics and Computer Science, University of Catania

  Sep 27, 15:30 - 17:00

  B1 L3 R3119

  modeling sorption kinetics

  An efficient method is proposed for the numerical solution of the Stokes equations in a domain with a moving bubble and two techniques for the treatment of the boundary conditions are adopted and then compared. The treatment of diffusion of surfactants (anions and cations) in presence of an oscillating bubble is an interesting interdisciplinary problem, with applications to chemistry and biology.
• 

  Deep Learning and Scientific Computing

  Jinchao Xu, Professor, Applied Mathematics and Computational Science

  Sep 27, 12:00 - 13:00

  B9 L2 R2322

  Deep learning scientific computing

  In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.
• 

  Solution Processable Carbon Nanotubes Based Sensors: Towards 4D Printed Electronics

  Lana Joharji, M.S. Student, Electrical and Computer Engineering

  Sep 26, 13:00 - 14:00

  B2 L5 R5209

  3D printing 4D printing

  In this thesis, we focus on the design and development of 4D printed sensors. Carbon nanotubes (CNTs) are used as the active sensing medium as they have proven to be ideal for application in sensors due to their high electric conductivity, stability, and mechanical flexibility. The effect of a heat-shrinkable substrate on the electronic and structural properties of CNTs is analyzed in depth, followed by the application in temperature, humidity, and pressure sensors. The results show that the 4D effect results in a more porous yet more conductive film due to an increase in the charge carrier concentration, enabling an improved sensitivity of the devices and allowing us to tune the selectivity based on the shrinking percentage. The developed device was fabricated using a rapid, cost-effective technique that is independent of advanced fabrication facilities to expand its applications to low-resource settings and environments.
• 

  Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

  Prof. Dhabaleswar K. (DK) Panda, Professor, Computer Science and Engineering, The Ohio State University

  Sep 26, 12:00 - 13:00

  B9 L2 R2322 H1

  data science Deep learning machine learning artificial intelligence

  This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.
• 

  Integrated Sensors: The synergistic combination of materials and microelectronics - 2022

  Khaled Salama, Professor, Electrical and Computer Engineering

  Sep 25, 12:00 - 13:00

  B9 L2 R2322 H1

  Integrated Sensors

  In this talk we will present the design and implementation of hybrid integrated sensors using integrated circuits. We will discuss the advantages and shortcomings of sensors built in silicon-based fabrication processes and examine, in detail, their integrated circuit topologies. We will conclude with examples of solutions that worked in the field which we domnetarted at KAUST.

Sep 18 - Sep 24, 2022

• 

  Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

  Daniel Paulin, Assistant Professor, School of Mathematics, University of Edinburgh.

  Sep 20, 15:30 - 17:00

  B1 L3 R3119

  Monte Carlo MCMC

  In this paper, we propose a detailed theoretical study of one of these algorithms known as the split Gibbs sampler. Under regularity conditions, we establish explicit convergence rates for this scheme using Ricci curvature and coupling ideas. We support our theory with numerical illustrations.
• 

  Waves Functional Materials: From theory to applications

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Sep 20, 12:00 - 13:00

  B9 L2 R2322

  waves Wave functional materials Applications

  No abstract is available.
• 

  Towards Detailed Understanding of the Visual World

  Fahad Khan, Associate Professor at MBZUAI and Linköping University

  Sep 19, 12:00 - 13:00

  B9 L2 R2322

  Computer Vision

  Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.
• 

  Pulse Shaped Waveform Characterization using the Schrödinger Operator's Spectrum

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

  Sep 18, 14:00 - 16:00

  B1 R4214

  biomedical signal analysis MIMIC II database

  Pulse-shaped signal characterization is a fundamental problem in signal processing. One recently developed tool available to analyze non-stationary pulse-shaped waveforms with a suitable peak reconstruction is semiclassical signal analysis (SCSA). SCSA is a signal representation method that decomposes a real positive signal y(t) into a set of squared eigenfunctions through the discrete spectrum of the Schrödinger operator which is of particular interest. Beginning with an introduction to the young method, this dissertation discusses the relevant properties of SCSA and how they are utilized in signal denoising and biomedical application. Based on this, different frameworks and methodologies are proposed to leverage the advantages of the SCSA, especially in the pulse-shaped signal analysis field.
• 

  Fully Printed, Flexible and Disposable Wireless Sensors for Internet of Things Applications

  Atif Shamim, Program Chair, Electrical and Computer Engineering

  Sep 18, 12:00 - 13:00

  B9 L2 R2322

  Fully Printed Flexible and Disposable Wireless Sensors

  With the advent of wearable devices and internet of things (IoT), there is a new focus on sensing systems which can be bent so that they can be worn or mounted on non-planar objects.
• 

  Recent Advances in Photonic Devices Modeling & Applications

  Salah Obayya, Professor and Director of Center for Photonics and Smart Materials (CPSM) Zewail City of Science and Technology

  Sep 18, 11:00 - 12:00

  B1 L3 R3119

  Micro-and Nano-Fabrication Techniques of Complex Photonic Devices

  Dr. Obayya will present the most recent research results achieved at the Center for Photonics and Smart Materials (CPSM), Zewail city, in connection with analysis, design and optimization of wide range of photonic devices with applications in optical communications, plasmonics, metamaterials, energy harvesters, optical biosensors, and many others.

Sep 11 - Sep 17, 2022

• 

  Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

  Dmitry Kovalev, Ph.D. Student, Computer Science

  Sep 14, 16:00 - 18:30

  B5 R5220

  optimization machine learning

  In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.
• 

  On the resolution of a theoretical question related to the nature of local training in federated learning

  Peter Richtarik, Professor, Computer Science

  Sep 13, 15:30 - 17:00

  B1 L3 R3119

  machine learning mathematical optimization communications algorithms

  In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.
• 

  Architecting the Future of Wireless Networks Connectivity

  Basem Shihada, Professor, Computer Science

  Sep 13, 14:00 - 15:30

  B9 L2 H2

  In this talk, I will start by providing our vision for next-generation networks. Throughout the talk, I will highlight several challenges in existing communication technologies that could have the potential of shaping new research and deployment directions of future wireless networks, including, (i) review our recent advances in non-terrestrial networks, which includes both UAVs and satellite (ii) show satellite systems are essential for today’s traffic-intensive applications while maintaining an accepted end-to-end latency for delay-sensitive applications and (iii) show how we integrated both existing Wi-Fi technology with optics to extend the Internet as we use it today to the underwater environments via Aqua-fi.
• 

  Solvers for Coupled Problems in Porous Media Science

  Arne Naegel

  Sep 13, 12:00 - 13:00

  B9 L2 R2322

  applied statistics

  No abstract is available.
• 

  Defeating the attacks from bots scrapers hiding behind residential proxy IPs

  Marc Dacier, Professor, Computer Science

  Sep 12, 12:00 - 13:00

  B9 L2 R2322 H1

  operational security resilient computing

  This talks presents a very serious emerging threat: the bots scraping web sites and hiding their IPs thanks to residential IP providers. The problem, state of the art and a new solution will be explained.
• 

  Raman characterization in Nitride

  Bei Ma, Electrical and Electronic Engineering, Chiba University, Japan

  Sep 11, 12:00 - 13:00

  B9 L2 R2322 H1

  Raman characterization in Nitride

  Raman spectroscopy has been used extensively to characterize the strain, defect density, and doping levels in semiconductors. We will introduce Raman spectroscopy in Nitride materials to estimate the crystal quality, and carrier concentration, which is determined by vibration modes.

Sep 4 - Sep 10, 2022

• 

  Pore-Scale Two-Phase Flows: Modeling, Algorithms, and Applications

  Sep 6, 15:30 - 17:00

  B1 L3 R3119

  Multiphase flows

  Abstract Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together. Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation. Field-scale or Darcy-scale simulation has conventionally and routinely used
• 

  Low Rank Everywhere (Almost)

  David Keyes, Professor, Applied Mathematics and Computational Science

  Sep 6, 12:00 - 13:00

  B9 L2 R2322

  computational science low rank hierarchical low rank data sparsity

  Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.
• 

  Communication-Efficient Distributed Newton-type Algorithms for Federated Learning

  Mher Safaryan, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Sep 5, 12:00 - 13:00

  B9 L2 R2322 H1

  machine learning

  In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.
• 

  Resilience is the Cybersecurity of the XXI century, and here's why! - 2022-09-04

  Sep 4, 12:00 - 13:00

  B9 L2 R2322

  Resilience is the Cybersecurity of the XXI century

  In a nutshell, Resilient Computing is a new paradigm based on modelling, architecting and designing computer systems so that: they have built-in baseline defences; such defences cope with virtually any quality of threat, be it accidental faults, design errors, cyber-attacks, or unexpected operating conditions; provide incremental protection of, and automatically adapt to, a dynamic range of threat severity; provide sustainable operation.

Aug 28 - Sep 10, 2022

• 

  Compound Semiconductor Weekend

  Integrated Photonics Laboratory; Smart, Advanced Memory devices and Applications Laboratory; Photonics Laboratory; Advanced Semiconductor Laboratory

  Sep 3, 09:00 - Sep 5, 17:00

  B3 L5 R5209

  The Compound Semiconductor Weekend will focus on some of the most important research areas on the beautiful KAUST campus by the Red Sea. It features 18 invited and contributed oral talks and a poster competition. Semiconductor technology is a major foundation of the third and fourth Industrial Revolutions as well as the modern society. While silicon remains as the widest-employed semiconductor, compound semiconductors have emerged from lab research to becoming the second most widely used within a short period of time. This is because compound semiconductors could possess multiple superior key properties simultaneously. They include high speed, high power, and efficient light emission and absorption, as well as that they can be produced in large scale at low cost and are highly robust. As a result, they have become cornerstones of many crucial technologies including lighting, display, communication, space exploration, and electric vehicles. While semiconductor researchers are plowing deeper for those technologies, compound semiconductors attract increasing attention for future computing, memory, and quantum information science as the pursuit of Moore’s Law has slowed down dramatically when the size of silicon transistors is approaching the physical limit of a few atoms.

Aug 28 - Sep 3, 2022

• 

  Emergence of transportation networks as a self-regulated process

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Science

  Aug 30, 15:30 - 17:00

  B1 L3 R3119

  random process transportation networks

  In this talk, we shall explain how transportation networks emerge as a self-regulating process, with a particular focus on applications in biology (leaf venation in plants, neuronal networks in animals). We start by introducing a purely diffusive model with tensor-valued diffusivity, derived as a gradient flow of a broad class of entropy dissipations. The introduction of a prescribed electric potential leads to the Fokker-Planck equation. We show that with quadratic entropy density modeling Joule heating, the model is convex with respect to the diffusivity tensor.
• 

  Introduction to AMCS / STAT Graduate Seminar 398

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Aug 30, 12:00 - 13:00

  B9 L2 R2322

  In this section, prof. Gabriel will be introducing the graduate seminar and its requirements.
• 

  Which came first: the paper or the peer-review?

  Marco Canini, Professor, Computer Science

  Aug 29, 12:00 - 13:00

  B9 L2 R2322 H1

  This talk presents some perspectives on doing research in computer science and publishing through the peer review process.
• 

  Towards Sustainable Cities: Enabled by City Scale Digital Twins

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Aug 28, 12:00 - 13:00

  B9 L2 H1

  Towards Sustainable Cities

  The remainder of the talk will focus on an ongoing effort to assess a city’s ecological impact through city scale digital twins.

Jul 24 - Jul 30, 2022

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  Communications, Computing, and Security for the Internet of Things Era and Beyond

  Hesham Elsawy, Assistant Professor, Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM)

  Jul 26, 16:00 - 17:00

  B1 L3 R3119

  communications computing

  This talk will first discuss the communications, computation, and security challenges for the IoT era and motivate a systematic methodology to address them. The talk will then overview some relevant problems and their potential solutions. Future research direction will also be highlighted.

Jul 17 - Jul 23, 2022

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  Ultra-Dense LEO Satellite-based Communication Systems: A Novel Modelling Technique

  Mustafa Kishk, Assistant Professor Maynooth University, Electronic Engineering Department

  Jul 20, 16:00 - 17:00

  B1 L3 R3119

  Stochastic Geometry Energy Harvesting Wireless Networks UAV-enabled Communication Systems

  In this talk, we discuss a recently proposed mathematical framework that enables tractable analysis of LEO satellite-enabled communication systems while capturing the influence of satellites’ numbers and altitudes as well as the spatial distribution of earth stations. Firstly, we describe how the stochastic geometry-based framework is modeled and discuss its accuracy. Next, we provide a detailed example of where this framework can be used for coverage analysis. Furthermore, we discuss how this framework can be used to study routing and end-to-end latency analysis in such networks. Realistic values from existing constellations, such as OneWeb and Starlink, are further used as case studies in this talk.
• 

  Liquid Metal-Based Inertial Sensors for Motion Monitoring and Human Machine Interfaces

  Wedyan Babatain, Ph.D. Student, Electrical and Computer Engineering

  Jul 19, 15:00 - 16:00

  B2 B3 A0215

  nanofabrication flexible electronics wearable devices sensors

  In this dissertation, the development of novel practical resistive and capacitive-type inertial sensors using liquid metal as a functional proof mass material is presented. Utilizing the unique electromechanical properties of liquid metal, the novel inertial sensor design confines a graphene-coated liquid metal droplet inside tubular and 3D architectures, enabling motion sensing in single and multiple directions. Combining the graphene-coated liquid metal droplet with printed sensing elements offers a robust fatigue-free alternative material for rigid, proof mass-based accelerometers. Resistive and capacitive sensing mechanisms were both developed, characterized, and evaluated. Emerging rapid fabrication technologies such as direct laser writing and 3D printing were mainly adopted, offering a scalable fabrication strategy independent of advanced microfabrication facilities. The developed inertial sensor was integrated with a programmable system on a chip (PSoC) to function as a stand-alone system and demonstrate its application for real-time- monitoring of human health/ physical activity and for soft human-machine interfaces. The developed inertial sensor architecture and materials in this work offer a new paradigm for manufacturing these widely used sensors that have the potential to complement the performance of their silicon counterparts and extend their applications.
• 

  Bruce Lee Inspired Fluid Antenna Systems for 6G

  Prof (Kit) Kai-Kit Wong, Professor of Wireless Communications, Department of Electronic and Electrical Engineering, University College London, UK

  Jul 18, 16:30 - 17:30

  B1 L3 R3119

  6G communications

  In this talk, I will speak on a novel antenna technology, referred to as fluid antenna, that adopts a software-controlled, position-flexible antenna to operate on the best signal envelope within a given space. This talk presents some early results on fluid antenna systems, which shows its potential for improving wireless communication performance.

Jul 3 - Jul 9, 2022

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  Integrated Photonic Devices for Sensing and Telecom Applications

  Prof. Hugo E. Hernandez Figueroa, University of Campinas (UNICAMP)

  Jul 6, 10:00 - 11:30

  B3 L5 R5209

  Integrated Photonics technology

  This talk will be focused on Integrated Photonics technology, which demands strong and robust confinement of light in order to attain the maximum possible integration of photonics devices on a single chip. Keeping this in mind, three applications based on LEMAC’s recent developments will be presented in this talk.
• 

  Mean-Field Games on Networks and Wardrop Equilibria - PhD Dissertation Defense

  Fatimah H. Al Saleh, PhD Student, Applied Mathematics and Computational Sciences, KAUST, Saudi Arabia

  Jul 6, 10:00 - 12:00

  KAUST

  This thesis consists of three main parts. In the first part, we discuss first-order stationary mean-field games (MFGs) on networks. In the second part, we discuss the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary MFGs. First, we build the Wardrop model on networks. Second, we show how to convert the MFG model into a Wardrop model. Next, we recover the MFG solution from the Wardrop solution. Finally, we study the calibration of MFGs with Wardrop travel cost problems. In the third part, we explain the algorithm for solving the algebraic system associated with the MFG numerically, then, we present some examples and numerical results.