Events

Nov 6 - Nov 13, 2022

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  Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

  Tobias Isenberg, Senior Research Scientist, Inria

  Nov 7, 12:00 - 13:00

  B9 L2 R2322 H1

  virtual reality data visualisation Scientific Visualization

  In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
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  Multivariate Functional Data Analysis and Visualization

  Zhuo Qu, Ph.D. Student, Statistics

  Nov 7, 11:00 - 13:00

  B3 L5 R5220

  functional data analysis multivariate functional data statistics

  As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

Oct 30 - Nov 13, 2022

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  Integrating Terrestrial and Non-terrestrial Networks: 3D Opportunities and Challenges

  Giovanni Geraci, Assistant Professor, Pompeu Fabra University, Barcelona

  Nov 6, 16:00 - 17:00

  B1 L3 R3119

  Integrating Terrestrial and Non-terrestrial Networks

  In a quest for anything, anytime, anywhere connectivity, next-generation wireless networks are envisioned to break the boundaries of the current ground-focused paradigm and fully embrace aerial and spaceborne communications.
• 

  Towards sustainable and resilient multi-energy infrastructure systems: water-energy nexus

  Mohannad Alhazmi, Electrical Engineering, King Saud University

  Nov 6, 12:00 - 13:00

  B9 L2 R2322 H1

  With the widespread deployment of advanced heterogeneous technologies and the sharply-growing complexity in our modern society, there is an increasing demand for risk-aware management and joint operation of interconnected infrastructures and lifeline networks.

Oct 30 - Nov 6, 2022

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  Flexible Electronics Beyond Mechanical Flexibility: Multifunctional Flexible Semiconductor Materials and Devices for Photonic, Electronic, Energy, and Sensing Applications

  Prof. Jae-Hyun Ryou, Mechanical Engineering, University of Houston

  Nov 3, 12:00 - 13:00

  B9 L3 R3223

  flexible photonic energy sensing devices

  Flexible electronics is an emerging and widely explored area. Most research groups in the area focus on fabrication processes to provide mechanical flexibility and their use in bendable and stretchable applications. Also, most semiconductors employed in flexible electronics are non-single-crystalline thin films which compromise the performance of the flexible devices.
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  Wide Bandgap Semiconductor Device Design via Machine Learning

  Rongyu Lin, Ph.D., Electrical and Computer Engineering

  Nov 2, 15:30 - 17:30

  B2 L5 R5220

  machine learning light gradient boosting machine extreme gradient boosting

  This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.
• 

  Autonomy for recreational vessels: Safe intelligence at your finger tips Pablo Afman, ATL Robotics

  Juan-Pablo Afman

  Nov 2, 12:00 - 13:00

  B2 L5 R5220

  robotics intelligent systems

  Abstract Thanks to ever improving sensing, computing, and actuating technologies, autonomy is fast becoming a practical tool for many marine applications. Pushed by the general interest in autonomy and attracted by its underlying capabilities, this talk will present robotic technologies that enable safety and convenience for recreational boaters and marine professionals alike. Beginning with a Grady-White offshore fishing boat powered by two 425 HP engines, we present demonstrated functionalities such as collision avoidance, auto-docking, dynamic positioning, track and follow, and "take me
• 

  Numerical approximation of McKean-Vlasov SDEs via Stochastic Gradient Descent

  Prof. Gonçalo dos Reis, School of Mathematics, University of Edinburgh

  Nov 1, 15:30 - 17:00

  B1 L3 R3119

  stochastic gradient descent McKean-Vlasov SDEs

  We propose a novel approach of numerically approximate McKean-Vlasov SDEs that avoids the usual interacting particle approximation and Propagation of Chaos results altogether.
• 

  Multilevel Bayesian Neural Networks

  Nov 1, 12:00 - 13:00

  B9 L2 R2322

  Bayesian computational technique Applied Machine Learning

  Abstract In this talk we consider the application of multilevel Monte Carlo for Bayesian computation tasks in machine learning. There has recently been a synergy of statisticsand machine learning, promoting the application and development of new methodologies.Based on this we promote the use of multilevel Monte Carlo, which is a technique used toreduce the cost to attain a particular order of MSE with trace-class neural network priors. We provide some theoretical insights, and demonstrate the performance of our methodologyon different model problems such as classification and reinforcement
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  Microstructural Analysis and Engineering of III-Nitride-Based Heterostructures for Optoelectronic Devices

  Martín Velázquez Rizo, Ph.D. Student, Electrical and Computer Engineering

  Nov 1, 10:00 - 11:30

  B1 L3 R3119

  photocatalytic energy conversion

  This dissertation presents a variety of methodological approaches to characterize, from a microstructural point of view, different properties of novel III-nitride-based heterostructures and devices. The results of the various characterizations contributed to developing novel LEDs and photocatalysts. The analyses and results presented in this dissertation strongly relied on the analytical capabilities offered by transmission electron microscopy, which proved to be a convenient and versatile tool for the characterization of many aspects related to the fabrication of III-nitride-based optoelectronic devices.
• 

  Everything PhD students always wanted to know about research* (*but were afraid to ask)

  Oct 31, 12:00 - 13:00

  B9 L2 R2322 H1

  scientific research PhD student

  From my experience, I will try to answer doubts and dilemmas PhD students are often faced with, in their path towards a degree. Namely, I'll discuss how advisors, colleagues, peers, reviewers and so forth, fit in the universe of a PhD student, and I will end sharing my own definition of 'excellence', as an objective to pursue.
• 

  The KAUST 2022 Workshop on Statistics

  Oct 31, 09:00 - Nov 3, 17:00

  B2 B3 A0215

  statistics data science big data modeling complex data

  The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

Oct 23 - Nov 6, 2022

• 

  Non-Terrestrial Networks: Opportunities and Challenges

  Abdulah Aljohani, Research and Innovation Consultant, Communications and Information Technology Commission (CITC)

  Oct 30, 16:00 - 16:30

  KAUST

  Non-Terrestrial Networks

  Next-generation wireless networks are envisioned to break the boundaries of the current terrestrial-based networks and fully adopt the Non-Terrestrial Networks (NTNs).
• 

  Toward scalable and high performance on-chip light source for integrated silicon photonics

  Chen Shang, Postoctoral Researcher Fellow, University of California Santa Barbara

  Oct 30, 12:00 - 13:00

  B9 L2 R2322

  Integrated Photonics technology

  Monolithic integration of quantum dot (QD) gain materials onto Si photonic platforms via direct epitaxial growth is considered as the ultimate solution for on-chip light sources.

Oct 23 - Oct 30, 2022

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  Enabling the New Semiconductor Revolution through Advances in Design-for-Yield, Design-for-Test, and Fault-Origin Analysis

  Krish Chakrabarty, John Cocke Distinguished Professor, Electrical and Computer Engineering, Duke University

  Oct 26, 11:15 - 12:15

  B9 L2 R2322

  advanced semiconductors

  In this talk, I will first provide an overview of our research on design-for-testability of 3D integrated circuits, silicon lifecycle management, microfluidics, and hardware security, which all involve close industry collaborations. I will next describe in more detail our recent work on design-for-yield that targets manufacturing imperfections for layouts based on emerging carbon nanotube field-effect transistors. Following this, I will present our ongoing work on built-in self-test of monolithic 3D integrated circuits. Finally, I will describe a test and diagnosis technique to characterize fault origins in inter-tier vias and resistive random-access memories for monolithic 3D integration.
• 

  On Some PDEs with singular data

  Prof. Mohammed Kbiri Alaoui, King Khalid University

  Oct 25, 15:30 - 17:00

  B1 L3 R3119

  PDEs singular data

  The aim of this talk is to investigate the following non local p-Laplacian problem with data a bounded Radon measure.
• 

  Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

  Zhongwei Zhang, Ph.D. Student, Statistics

  Oct 25, 15:00 - 18:00

  B5 L5 R5220

  Statistics of extremes spatial statistics

  This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.
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  A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Public Colloquium

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Oct 25, 14:00 - 16:00

  B9 L2 R2322

  Applied complexity artificial intelligence information security biomedical applications

  Applied Complexity aims to understand the physical origin of these behaviors and transform them into sustainable technologies that tackle global problems of global interest. These range from energy harvesting to clean water production, the design of smart materials, biomedical applications, information security, artificial intelligence, and global warming. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.
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  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.
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  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.

Oct 16 - Oct 30, 2022

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  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 23, 2022

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  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 Sciences

  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 16, 2022

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  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.
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  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, Electrical and Mathematical Sciences and Engineering

  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.

Oct 2 - Oct 16, 2022

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  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.
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  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 9, 2022

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  Towards Structured Intelligence with Deep Graph Neural Networks

  Guohao Li, Ph.D. Student, 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.
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  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.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  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.
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  A new class of random field models for data on networks

  David Bolin, Associate 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.

Sep 25 - Oct 9, 2022

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  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 2, 2022

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  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.
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  Deep Learning and Scientific Computing

  Jinchao Xu, Professor, Applied Mathematics and Computational Sciences

  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.
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  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.
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  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.

Sep 18 - Oct 2, 2022

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  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 25, 2022

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  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.
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  Waves Functional Materials: From theory to applications

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Sep 20, 12:00 - 13:00

  B9 L2 R2322

  waves Wave functional materials Applications

  No abstract is available.
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  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.

Sep 11 - Sep 25, 2022

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  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.
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  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.
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  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 18, 2022

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  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.
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  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.
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  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.