Events

Oct 10 - Oct 16, 2021

• 

  The Data-sparse Renaissance in Numerical Linear Algebra

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  Oct 11, 12:00 - 13:00

  B9 R2322 H1

  A traditional goal of algorithmic optimality, squeezing out operations, has been superseded because of evolution in architecture. Algorithms must now squeeze memory, data transfers, and synchronizations, while extra operations on locally cached data cost relatively little time or energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity in approximating a wide class of formally dense operators that arise in exascale applications.
• 

  Theory and Application of Neural Networks I

  Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University

  Oct 11, 09:00 - 10:00

  B4 B5 A0215

  I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.
• 

  Present and Future of Reconfigurable Intelligent Surface-Empowered Communications

  Ertugrul Basar, Associate Professor, Electrical and Electronics Engineering, Koç University

  Oct 10, 12:00 - 13:00

  KAUST

  Present and Future of Reconfigurable Intelligent Surface-Empowered Communications

  Signal processing and communication communities have witnessed the rise of many exciting communication technologies in recent years. Notable examples include alternative waveforms, massive multiple-input multiple-output (MIMO) signaling, non-orthogonal multiple access (NOMA), joint communications and sensing, sparse vector coding, index modulation, and so on.

Oct 3 - Oct 9, 2021

• 

  Quantifying Uncertainty with a Derivative Tracking SDE Model and Application to Wind Power Forecast Data

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  Oct 7, 12:00 - 13:00

  KAUST

  We develop a data-driven methodology based on parametric Itô's Stochastic Differential Equations (SDEs) to capture forecast errors' asymmetric dynamics, including the forecast's uncertainty at time zero.
• 

  On Seven Fundamental Optimization Challenges in Machine Learning

  Konstantin Mishchenko, Ph.D. Student, Computer Science

  Oct 4, 17:00 - 18:00

  KAUST

  In this thesis, we discuss some new developments in optimization inspired by the needs and practice of machine learning, federated learning, and data science. In particular, we consider seven key challenges of mathematical optimization that are relevant to modern machine learning applications, and develop a solution to each.
• 

  Bitcoin and decentralized protocols

  Ricardo Pérez-Marco, Visiting Professor at KAUST, CNRS researcher in Paris

  Oct 4, 12:00 - 13:00

  B9 R2322 H1

  About 12 years ago, Bitcoin was created as the first form of decentralized money, with some of the properties of Nash's ideal money. The protocol proposes a novel probabilistic consensus mechanism, that has the potential to automatize and decentralize many other human activities. The Bitcoin network also provides the first decentralized clock, and has a rich statistical physics interpretation. We will explore the foundations of "Decentralization Theory" and explore what can be expected as future developments.
• 

  Visible light-emitting diodes for next-generation displays

  Daisuke Iida, Senior Research Scientist, Electrical and Computer Engineering

  Oct 3, 12:00 - 13:00

  KAUST

  Visible light emitting diodes for next generation displays

  This talk will provide an overview of the III-nitride-based visible light-emitting diodes (LEDs). Especially, the InGaN-based blue LEDs are very contributed to energy-saving for light sources all over the world.

Sep 26 - Oct 2, 2021

• 

  Frequency Control and stability for Low-Inertia Systems

  Ha Thi Nguyen, Assistant Research Professor, Electrical and Computer Engineering, University of Connecticut

  Sep 30, 15:30 - 16:30

  KAUST

  synchronous condenser synthetic inertia real time digital simulator

  The rapid penetration of renewable energy sources (RESs) into power systems has introduced many challenges in securing a stable network operation and has forced power systems to operate in low inertia conditions. Low short-circuit power and intrinsic inertial response from converter-interfaced RES may cause poor dynamic performance of systems and render the system frequency more vulnerable than conventional grids. The talk proposes and analyzes different strategies using synchronous condenser (SC), synthetic inertia (SI) of wind power plant, and their combination to enhance the frequency stability of low inertia systems under various scenarios and wind conditions.
• 

  Discriminant analysis: Fundamental limits and Optimal designs

  Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering

  Sep 30, 12:00 - 13:00

  KAUST

  Despite the recent advances in big data processing, enabled by the emergence of large-scale machine learning techniques, several statistical questions regarding the behavior in the regime of high dimensions of well-established and fundamental methods have remained unresolved.
• 

  Randomized multilevel Monte Carlo for inference

  Kody J.H. Law, Professor, Applied Mathematics in the Department of Mathematics, University of Manchester and Manchester Institut

  Sep 29, 13:00 - 14:00

  KAUST

  Often in the context of data centric science and engineering applications, one endeavours to learn complex systems in order to make more informed predictions and high stakes decisions under uncertainty. Some key challenges which must be met in this context are robustness, generalizability, and interpretability.
• 

  Accurate and Efficient Methods to Simulate Electromagnetic Systems

  Hakan Bagci, Associate Dean for Students, Computer, Electrical and Mathematical Sciences and Engineering

  Sep 28, 16:00 - 17:30

  B9 L2 R2322

  Simulation tools are often enablers of cutting-edge research in many fields of science and engineering. This also applies to the field of electromagnetics: Design and characterization of many electromagnetic systems and devices, which drive technological advances in areas such as communications, computing, biomedicine, and solar energy, would not be possible without simulation tools. Having said that, developing numerical methods for electromagnetic analysis of such complex systems is not a trivial task. Dimensions of these systems are large (longer than several wavelengths), their frequency of operation has a wide range, and their geometry has sub-wavelength features. When it comes to simulating these systems by solving the Maxwell equations (in differential or integral form), these characteristics translate into multiscale discretizations, large and often ill-conditioned matrix systems with millions of unknowns to be solved for, and long execution times. My research group at KAUST has been developing efficient, accurate, and robust electromagnetic solvers to address these challenges. In this talk, I will provide an overview of my group’s recent research activities. Technical part of my presentation will focus on two examples, where I talk about solvers we have developed to efficiently and accurately simulate photoconductive antennas (used in terahertz source generation) and plasmonic structures (with a wide range of applications from sensing to solar energy). I will provide results that demonstrate the benefits of these solvers over existing methods. I will conclude my talk with a brief description of my future research plans and a few slides about my research supervision, teaching activities, and international visibility of my research group.
• 

  Towards Secure and Resilient Cyber-Physical Energy Systems - 2021-09-27

  Charalambos Konstantinou, Assistant Professor, Computer Science, Electrical and Computer Engineering

  Sep 27, 12:00 - 13:00

  B9 R2322 H1

  This talk will give an overview of the research of the Secure Next Generation Resilient Systems (SENTRY) lab (sentry.kaust.edu.sa) at KAUST. The transformation of critical infrastructures into cyber-physical systems contributes towards modernization allowing for better planning, more flexible control, system-wide optimization, etc. The security, however, of such systems presents significant challenges in controlling and maintaining secure access to critical system resources and services.
• 

  Advanced Ga2O3 flexible and electronic devices

  Xiao Tang, Research Scientist, Electrical and Computer Engineering

  Sep 26, 12:00 - 13:00

  KAUST

  Nowadays, the development of portable and wearable electronics becomes unprecedentedly significant because they allow for real-time communication, remote controlling, and monitoring of various potential environmental hazards regardless of location and time.

Sep 19 - Sep 25, 2021

• 

  Network-based clustering approaches for integrative multiomics data and drug response prediction

  Zehor Belkhatir, Senior Lecturer, Control Engineering, De Montfort University

  Sep 20, 15:00 - 16:00

  B1 L2 H2

  multiomics data integration Wasserstein distance gene ontology

  Biological data sets, such as gene expressions, copy number alteration, and pharmacogenomics, are often high-dimensional and thus difficult to analyze and interpret. This talk presents two data analysis methodologies that we recently developed based on network analysis via Wasserstein optimal transport combined with unsupervised classification techniques.
• 

  Deep Optics - Joint Design of Imaging Hardware and Reconstruction Methods - Graduate Seminar

  Wolfgang Heidrich, Professor, Computer Science

  Sep 20, 12:00 - 13:00

  B9 R2322 H1

  Classical imaging systems are characterized by the independent design of optics, sensors, and image processing algorithms. In contrast, computational imaging systems are based on a joint design of two or more of these components, which allows for greater flexibility of the type of captured information beyond classical 2D photos, as well as for new form factors and domain-specific imaging systems. In this talk, I will describe how numerical optimization and learning-based methods can be used to achieve truly end-to-end optimized imaging systems that outperform classical solutions.
• 

  Resilience is the Cybersecurity of the XXI century, and here's why! - 2021-09-19

  Sep 19, 12:00 - 13:00

  KAUST

  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. Resilient Computing will be a game changer in the craft of designing computer systems of today and future.

Sep 12 - Sep 18, 2021

• 

  Audio Event Recognition for Automatic Acoustic Monitoring

  Farook Sattar, Electrical and Computer Engineering, University of Victoria, Canada

  Sep 16, 16:00 - 17:00

  KAUST

  bioacoustics acoustic signals audio event recognition

  Nowadays acoustic sensors collect huge amount of data on a regular basis to monitor various activities. This necessitates automated audio event recognition using acoustic data. In this talk, we will present effective methods for detecting and classifying different audio events. Interesting ideas and results will be shown for the proposed methods related to biological/bioacoustic signals.
• 

  Developing Aramco’s Next Generation Operational Systems for the Red Sea and the Arabian Gulf

  Ibrahim Hoteit, Professor, Earth Science and Engineering

  Sep 16, 12:00 - 13:00

  KAUST

  The talk will present our efforts to develop the next generation operational systems for the Red Sea and the Arabian Gulf, as part of Aramco’s resolution toward the Fourth Industrial Revolution. These integrated systems, we refer to as iReds and iGulf, have been built around state-of-the-art ocean-atmosphere-wave general circulation models that have been specifically developed for the region and nested within the global weather systems.
• 

  End-to-end Optics Design for Computational Cameras

  Qilin Sun, Ph.D. Student, Electrical and Computer Engineering

  Sep 15, 16:20 - 18:10

  KAUST

  Imaging systems have long been designed in separated steps: the experience-driven optical design followed by sophisticated image processing. Such a general-propose approach achieves success in the past but left the question open for specific tasks and the best compromise between optics and post-processing, as well as minimizing costs. Driven from this, a series of works are proposed to bring the imaging system design into end-to-end fashion step by step, from joint optics design, PSF optimization, phase map optimization to a general end-to-end complex lens camera.
• 

  A Journey in the Underwater Systems - Graduate Seminar

  Basem Shihada, Professor, Computer Science

  Sep 13, 12:00 - 13:00

  B9 R2322 H1

  In this seminar, I will go over our journey in the underwater networks research work. Basically, I will highlight our recent work on bringing the Internet to underwater environments by deploying a low power and compact underwater optical wireless system, called Aqua-Fi, that support today’s Internet applications.
• 

  Multiphysics Simulation of Semiconductor Optoelectronic Devices

  Liang Chen, Research Scientist, Electrical and Computer Engineering

  Sep 12, 12:00 - 13:00

  KAUST

  Multiphysics Simulation

  In this talk, I will introduce a simulator specifically formulated and implemented for photoconductive terahertz devices enhanced with nanostructures. The fundamental challenge in formulating this simulator is the rigorous modeling of the coupling mentioned above. My talk will focus on this aspect as well as the general concerns in building mathematical models for various physical processes and their numerical discretization. To this end, I will introduce a discontinuous Galerkin framework that can efficiently discretize the multiscale mathematical models formulated for coupled semiconductor physics and electromagnetics. Furthermore, I will present numerical results which demonstrate the applicability of this framework in characterizations of photoconductive terahertz antennas and photomixers.

Sep 5 - Sep 11, 2021

• 

  Empirical Risk Minimization in the Non-interactive LDP Model

  Di Wang, Assistant Professor, Computer Science

  Sep 9, 12:00 - 13:00

  KAUST

  As a fundamental problem in both machine learning and privacy, Empirical Risk Minimization in the Differential Privacy Model (DP-ERM) received much attentions. However, most of the previous studies are either in the central DP model or interactive LDP model. In this talk, I will discuss some recent developments of DP-ERM in the non-interactive LDP model.
• 

  Finite element approximation of fluid-structure interactions: a fictitious domain approach

  Lucia Gastaldi, Professor, Numerical Analysis at the University of Brescia, Italy

  Sep 7, 15:00 - 16:00

  B1 L4 R4102

  In this talk I report some results on the approximation of fluid-structure interaction problems using non matching grids. Our formulation originates from the Immersed Boundary Method and then moved toward the Fictitious Domain approach. The advantages of this formulation is that it avoids the difficulties related with mesh generation and it allows the treatment of fluid and solid in their natural Eulerian and Lagrangian framework. I present the well-posedness of our formulation at the continuous level in a simplified setting. Moreover, I shall discuss various time discretizations that provide unconditionally stable schemes and some computational details.
• 

  Computational Imaging and Its Applications in Fluids

  Jinhui Xiong, Ph.D. Student, Computer Science

  Sep 6, 16:00 - 17:00

  KAUST

  Computational imaging differs from traditional imaging systems by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D-3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color.
• 

  A Toolkit for Multi-Faceted Geospatial Data Protection in Transactional and Analytical Processing using Searchable Encryption and Differential Privacy

  Gabriel Ghinita, Associate Professor, University of Massachusetts, Boston

  Sep 6, 12:00 - 13:00

  B9 R2322 H1

  The mobile revolution of the past decade led to the ubiquitous presence of location data in all application domains, ranging from public safety and healthcare to urban planning, transportation and commercial applications. Numerous services rely on location data to provide customized service to their users. At the same time, there are serious concerns with respect to protecting individual privacy, as location traces can disclose sensitive details to an untrusted service.
• 

  Microwave Water Cut Sensors for Oil Industry: From Design to Deployment in the Field

  Muhammad A. Karimi, Chief Technology Officer, Saher Flow Solutions

  Sep 5, 12:00 - 13:00

  KAUST

  This talk will be focused on developing low-cost sensors, which can increase oil production efficiency through real-time monitoring of oil wells. The mechanism behind the development of low-cost & printed microwave sensors will be introduced followed by a number of applications where these sensors can be utilized. A specific application to measure the composition of production fluid will be described in detail. Unique benefits of Microwave DMOR technology, utilized in the sensors, will be explained in comparison with existing technologies.

Aug 29 - Sep 4, 2021

• 

  High-resolution Modeling and Estimation of Extreme Red Sea Surface Temperature Hotspots

  Raphaël Huser, Associate Professor, Statistics

  Sep 2, 12:00 - 13:00

  KAUST

  Modeling, estimation and prediction of spatial extremes is key for risk assessment in a wide range of geo-environmental, geo-physical, and climate science applications. In this work, we propose a flexible approach for modeling and estimating extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the whole Red Sea, a vital region of high biodiversity.
• 

  Sequential Monte Carlo Algorithms for Agent-Based Models of Disease Transmission

  Jeremy Heng, Applied Mathematics and Computational Sciences (AMCS), ESSEC Business School, Singapore

  Sep 1, 13:00 - 14:00

  KAUST

  We consider statistical inference for a class of agent-based SIS and SIR models. In these models, agents infect one another according to random contacts made over a social network, with an infection rate that depends on individual attributes. Infected agents might recover according to another random mechanism that also depends on individual attributes, and observations might involve occasional noisy measurements of the number of infected agents. Likelihood-based inference for such models presents various computational challenges. In this talk, I will present various sequential Monte Carlo algorithms to address these challenges.
• 

  Large-Scale Visualization of Scientific Data: Volume Data and Flow Fields - 2021-08-30

  Markus Hadwiger, Professor, Computer Science

  Aug 30, 12:00 - 13:00

  B9 R2322 H1

  This talk will give an overview of the research of the High-Performance Visualization research group (vccvisualization.org) at the KAUST Visual Computing Center (VCC). Interactive visualization is crucial to exploring, analyzing, and understanding large-scale scientific data, such as the data acquired in medicine or neurobiology using computed tomography or electron microscopy, and data resulting from large-scale simulations such as fluid flow in the Earth’s atmosphere and oceans. The amount of data in data-driven science is increasing rapidly toward the petascale and further.
• 

  Dark Vision: A Sensory Eye into Subsurface Wellbore Casings

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Aug 29, 12:00 - 13:00

  KAUST

  Dark Vision Sensory Eye Subsurface Wellbore

  After a quick overview of the ECE Graduate Seminar logistics, I will share a quick introduction to the wellbore construction process. This will help build the case for maintaining wellbore integrity in order to protect assets, people, the environment and production. The synergistic integration of electromagnetics, electronics and machine learning to create a novel mechatronic solution to address wellbore integrity needs is then discussed. The solution utilizes a full maxwell equations solver deployed on KAUST’s super computing platforms to enable next generation physics informed wellbore integrity solutions based on non-contact EM field propagation circuits. While downhole camera technologies are used today, they require illumination and an optically clear environment. Our electromagnetic ‘vision’ system overcomes these limitations and provides additional capability to ‘see through’ nested wellbore tubulars.

Aug 15 - Aug 21, 2021

• 

  Machine Learning in Healthcare: When Low Sample Size is not a Limitation

  Dr. Ricardo Henao, Biostatistics and Bioinformatics, Duke University

  Aug 17, 14:30 - 15:30

  KAUST

  In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.
• 

  Applications of Magnetic Transition Metal Dichalcogenide Monolayers to the Field of Spinorbitronics

  Idris H. Smaili, Ph.D. Student, Electrical and Computer Engineering

  Aug 16, 11:00 - 13:00

  KAUST

  flexible nanodevices flexible solar cells nanomaterials thin films density functional theory

  Magnetic random access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Consequently, there is now an urgent need for new low-dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writing, and storing data.
• 

  AMCS Workshop for Higher Education Instructors

  Aug 15, 10:00 - Aug 26, 16:00

  KAUST Campus

  linear algebra Programming language

  Workshop's Agenda View Workshop's Agenda. Topics for the event: Core sessions on Linear Algebra Core sessions on applications on emerging teaching methodologies Programming language (practical applications) Keynote lectures on emerging teaching methodologies and current research applications Core lab and research center visits and presentations Networking sessions and social activities with KAUST faculty, researchers, alumni, and students This workshop is for TAs and lecturers from in-Kingdom Universities. It is not open to KAUST students and the community. Applications are closed now!

Aug 8 - Aug 14, 2021

• 

  Stylistic and spatial disentanglement in GANs

  Yazeed Alharbi, Ph.D., Computer Science

  Aug 12, 14:00 - 16:00

  KAUST

  Computer Vision machine learning generative adversarial network Deep learning

  This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Jul 25 - Jul 31, 2021

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  Stochastic Optimization in Target Positioning and Location-based Applications

  Hui Chen, Ph.D. Student (former), Electrical and Computer Engineering

  Jul 27, 17:00 - 19:00

  KAUST

  stochastic algorithm optimization Hand Gestures Recognition

  This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

Jul 4 - Jul 10, 2021

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  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2C

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jul 7, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly
• 

  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2B

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jul 6, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly
• 

  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2A

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jul 5, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly

Jun 27 - Jul 3, 2021

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  ISC21 Workshop: Numerical Algorithms and Libraries for Exascale (NAL-X)

  Hatem Ltaief, Principal Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

  Jul 2, 14:00 - 18:00

  ISC21 KAUST Frankfurt Germany Time CET

  HPC numerical linear algebra parallel numerical algorithms Parallel and Distributed Computing GPU Computing Mixed Precisions

  Abstract With the hardware technology scaling and the trend on heterogeneous chip design, the existing numerical algorithms and software framework may break down due to load imbalance. There is currently a fundamental mismatch between the underlying hardware architecture with high thread concurrency and the software deployment of numerical libraries, which relies on the traditional bulk synchronous programming model. Numerical software should first squeeze performance out of single node by efficiently running on manycore architectures with processor counts sharing a common memory in the
• 

  State estimation for nonlinear dynamical systems: Methods and applications | Part 1C

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jun 30, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly
• 

  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 1B

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jun 29, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly
• 

  Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

  Robert Hoehndorf, Associate Professor, Computer Science

  Jun 27, 10:00 - 11:00

  B9 L2 H2

  artificial intelligence

  Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for the identification of gene-disease

Jun 13 - Jun 19, 2021

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  Reconfigurable Snapshot HDR Imaging Using Coded Masks

  Masheal Alghamdi, Ph.D., Computer Science

  Jun 17, 12:00 - 14:00

  KAUST

  High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this thesis, we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware combined with a deep learning algorithm to reconstruct the HDR image. In this approach, we achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible, yet robust, way to modulate per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparative analysis demonstrated that our method outperforms the state-of-the-art in terms of visual perception quality.
• 

  The envelope formula and its applications in variational analysis - Session 3

  Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

  Jun 16, 19:00 - 21:00

  KAUST

  I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
• 

  Learning over Graphs

  Ali H. Sayed, Dean of Engineering, EPFL Switzerland

  Jun 15, 16:30 - 17:45

  KAUST

  This talk explains how agents over a graph can learn from dispersed information and solve inference tasks of varying degrees of complexity through localized processing. The presentation also shows how information or misinformation is diffused over graphs, how beliefs are formed, and how the graph topology helps resist or enable manipulation. Examples will be considered in the context of social learning, teamwork, distributed optimization, and adversarial behavior.
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  The envelope formula and its applications in variational analysis - Session 2

  Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

  Jun 14, 19:00 - 21:00

  KAUST

  I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.

Jun 6 - Jun 12, 2021

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  An introduction to bifurcation theory and applications to MFG - Session 2

  Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy

  Jun 10, 14:00 - 17:00

  KAUST

  In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.
• 

  The envelope formula and its applications in variational analysis - Session 1

  Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

  Jun 9, 19:00 - 21:00

  KAUST

  I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
• 

  Research on Ultrawide Bandgap Semiconductors

  Xiaohang Li, Associate Professor, Electrical and Computer Engineering

  Jun 8, 15:00 - 16:30

  KAUST

  Wide bandgap (WBG) semiconductors including GaN have demonstrated great success in lighting, display, electrification, and 5G communication due to superior properties and decades of R&D. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) semiconductors with bandgap larger than GaN have attracted increasing attentions. They are regarded as the 4th wave of the inorganic semiconductors after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, crucial for sustainability and health of the human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. This seminar would cover the latest research by the Advanced Semiconductor Lab. It includes multi-disciplinary studies of growth, materials, physics, and devices of the UWBG semiconductors.
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  An introduction to bifurcation theory and applications to MFG - Session 1

  Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy

  Jun 8, 15:00 - 18:00

  KAUST

  In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.