Events

Oct 31 - Nov 14, 2021

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  Joint Quantile Disease Mapping for Areal Data

  Hanan Alahmadi, Ph.D. Student, Statistics

  Nov 7, 15:00 - 16:00

  B1 L4 R4102

  The statistical analysis based on the quantile method is more comprehensive, flexible, and not sensitive against outliers compared to the mean methods. The study of the joint disease mapping focuses on the mean regression. This means they study the correlation or the dependence between the means of the diseases by using standard regression. However, sometimes one disease limits the occurrence of another disease. In this case, the dependence between the two diseases will not be in the means but in the different quantiles; thus, the analyzes will consider a joint disease mapping of high quantile for one disease with low quantile of the other disease.
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  Lyapunov equations that go beyond Lyapunov equations for system stability and performance

  Eric Feron, Professor, Electrical and Computer Engineering

  Nov 7, 12:00 - 13:00

  KAUST

  Lyapunov equations

  Lyapunov's stability theory, which was formulated around 1892, presents itself as a watershed event in the study of dynamical systems.

Oct 31 - Nov 7, 2021

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  Joint modeling of dependent diverse format data: A review from several case-studies

  Giovani Silva, PhD in Mathematics (IST) at the University of Lisbon

  Nov 4, 12:00 - 13:00

  KAUST

  his talk reviews some fundamental and practical issues related to the formulation and analysis of joint models of mixed types of outcomes with latent variables, with particular emphasis on both several case-studies in applied statistics and their computational implementation
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  Particle Approximations For Partially Observed Fluid Dynamics Models

  Professor Dan Crisan, Mathematics, Imperial College London

  Nov 3, 15:00 - 16:00

  KAUST

  fluid dynamics data assimilation stochastic

  Fluid dynamics models are ubiquitous in a multitude of applications. One of the most important applications of fluid dynamics models is numerical weather prediction. Modern numerical weather prediction combines sophisticated nonlinear fluid dynamics models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational weather centers across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. I will give a short overview of the ongoing research that aims to drastically decrease the required DA computational effort by reducing the dimension of the models involved and using stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done by using particle approximations suitably adapted to solve high-dimensional problems.
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  Towards Generalized and Robust Knowledge Association

  Shichao Pei, Ph.D. Student, Computer Science

  Nov 2, 16:00 - 18:00

  KAUST

  Human knowledge can facilitate the evolution of artificial intelligence towards learning the capability of planning and reasoning and has been the critical element for developing the next-generation artificial intelligence. Although knowledge collection and organization have achieved significant progress, it is still non-trivial to construct a comprehensive knowledge graph for downstream applications. The difficulty motivates the study of knowledge association to resolve the problem, yet current solutions suffer from two primary shortages, i.e., generalization and robustness. Specifically, most existing methods require a sufficient number of labeled data and ignore the effective utilization of complex relationships between entities, limiting the generalization ability of knowledge association approaches. Moreover, prevailing approaches severely rely on clean labeled data, making the model vulnerable to noises in the given labeled data. These shortages motivate the research on generalization and robustness of knowledge association in this dissertation.
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  Control and Estimation for Partial Differential Equations and Extension to Fractional Systems

  Lilia Ghaffour , Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 1, 15:00 - 17:00

  KAUST

  Partial differential equations (PDEs) are used to describe multi-dimensional physical phenomena. However, some of these phenomena are described by a more general class of systems called fractional systems (FS).
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  EF21: A new, simpler, theoretically better, and practically faster error feedback

  Peter Richtarik, Professor, Computer Science

  Nov 1, 12:00 - 13:00

  B9 R2322 H1

  Error feedback (EF), also known as error compensation, is an immensely popular convergence stabilization mechanism in the context of distributed training of supervised machine learning models enhanced by the use of contractive communication compression mechanisms, such as Top-k. First proposed by Seide et al (2014) as a heuristic, EF resisted any theoretical understanding until recently [Stich et al., 2018, Alistarh et al., 2018].
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  High order schemes for hyperbolic balance laws - Topic 3

  Giovanni Russo, Full Professor, Mathematics and Computer Science, University of Catania, Italy

  Nov 1, 09:00 - 10:00

  B1 L4 R4102

  Semi-implicit schemes for evolutionary partial differential equations. Topic 3 - construction of more general schemes for evolutionary partial differential equations, in which the stiffness may be of a different type than the one previously considered. Several examples will be given illustrating the general procedure.

Oct 24 - Nov 7, 2021

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  Social Decision Making for Robot Navigation

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Oct 31, 12:00 - 13:00

  KAUST

  Social robot navigation

  Robot navigation typically comprises of decision making at two different levels - global planning to compute a viable trajectory to the robot's destination and strategic (local) interaction to elicit cooperation and resolve any conflicts with other robots/pedestrians that would arise while navigating along the trajectory. Robot navigation in crowded environments is particularly challenging as the robot needs to exhibit navigation behaviors that are conceived as socially compliant by human pedestrians or vehicles they maneuver at both of the levels. In this presentation, I will introduce some of relevant works from my research group.

Oct 24 - Oct 31, 2021

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  High-Bitrate Photodetection in Ultraviolet-to-Visible for Optical Wireless Communication

  Chun Hong Kang, Research Scientist, Electrical and Computer Engineering

  Oct 28, 16:00 - 17:00

  KAUST

  Optical wireless communication, taking advantage of the unlicensed ultraviolet-to-visible wavelength region of the electromagnetic spectrum, had been coined as the next-generation wireless communication technology and holds promises to deliver a boundless, high-speed, reliable and secured broadband experience.
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  Recent progress on symbolic computation for PDE and their numerical schemes

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Oct 28, 12:00 - 13:00

  KAUST

  The qualitative study of PDEs often relies on integral identities and inequalities. For example, for time-dependent PDEs, conserved integral quantities or quantities that are dissipated play an important role. In particular, if these integral quantities have a definite sign, they are of great interest as they may provide control on the solutions to establish well-posedness.
• 

  High order schemes for hyperbolic balance laws - Topic 2

  Giovanni Russo,Full Professor,Mathematics and Computer Science, University of Catania, Italy

  Oct 28, 09:00 - 10:00

  B1 L4 R4102

  Implicit-Explicit schemes for hyperbolic systems with stiff relaxation. Topic 2 - hyperbolic relaxation models and to the methods for their numerical solution. After introduction of hyperbolic-hyperbolic and hyperbolic-parabolic type relaxation problem, conservative finite difference space discretization will be introduced.
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  High order schemes for hyperbolic balance laws - Topic 1

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

  Oct 27, 09:00 - 10:00

  B1 L4 R4102

  Construction of high order finite volume and finite difference shock-capturing schemes for conservation laws. Topic 1 - illustrating how to construct shock capturing schemes for conservation laws. We focus on semi-discrete schemes based on the method of lines.
• 

  On supercomputers and engineering applications: DNS, modeling, and GPUs

  Mathis Bode, Researcher, Institute for Combustion Technology (ITV) at RWTH Aachen University

  Oct 26, 14:00 - 15:00

  B2 L5 R5209

  Navier-Stokes supercomputers computational fluid dynamics

  The numerical solution of multi-physics problems relying on the Navier-Stokes equations has kept multiple generations of supercomputers busy. For fundamental problems, computational fluid dynamic aims to resolve all relevant time and length scales, which is then known as direct numerical simulation (DNS).
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  Monotonicity method for Mean-Field games

  Teruo Tada, Ph.D. Student, Applied Mathematics and Computational Sciences

  Oct 25, 14:00 - 16:00

  B1 R4214

  During my Ph.D. program, we have studied mean-field games (MFGs). MFGs model games with large populations of rational agents. The agents search for their optimal strategies and trajectories to minimize an individual cost, which depends on the statistical distribution of the population. Although it is quite hard to consider the systems of large populations in the numerical analysis, we can expect to consider the average effect given by the populations because the influence of each agent should be small.
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  The Impact of Information Technology on the Future of Energy Consumption

  Elmootazbellah Elnozahy, Professor (former), Computer Science

  Oct 25, 12:00 - 13:00

  B9 R2322 H1

  The human race is facing what may turn out to be an existential threat due to entrenched practices that are contributing to climate change. This talk addresses the impact of information technology (IT) in this regard.

Oct 17 - Oct 31, 2021

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  Topologically Transformed Electronics for Internet-of-Things Applications

  Nazek El-Atab, Assistant Professor, Electrical and Computer Engineering

  Oct 24, 12:00 - 13:00

  KAUST

  Topologically Transformed electronics

  We live in the age of information where electronics play a critical role in our daily life. Moore’s Law: performance over cost has inspired innovation in complementary metal oxide semiconductor (CMOS) technology and enabled high performance, ultra-scaled CMOS electronics.
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  Regularity for singular and degenerate pdes: qualitative vs. sharp estimates

  José Miguel Urbano, Professor of Mathematics at the University of Coimbra, Portugal

  Oct 24, 12:00 - 13:00

  B9 L2 H2

  Singular and degenerate partial differential equations are unavoidable in the modelling of several phenomena, from phase transitions to flows in porous media or chemotaxis. They encompass a crucial issue in the analysis of pdes, namely wether we can still derive analytical estimates when the crucial algebraic assumption of ellipticity collapses.

Oct 17 - Oct 24, 2021

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  Enabling Accurate and Efficient Simulation in Visual Computing - Graduate Seminar - AMCS - STAT

  Dominik L. Michels, Associate Professor, Computer Science

  Oct 21, 12:00 - 13:00

  KAUST

  The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations.

Oct 10 - Oct 17, 2021

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  Bayesian image analysis in Fourier space (BIFS) models and some relationships with Markov random fields

  Prof. John Kornak, Biostatistics, University of California, San Francisco

  Oct 14, 16:30 - 17:45

  B4 B5 A0215

  bayesian analysis

  Abstract For over 30 years, Bayesian image analysis has provided an important pathway to image reconstruction and enhancement, by balancing a priori expectations of image characteristics with a model for the noise process. The conventional Bayesian modeling approach defined in image space implements priors that describe inter-dependence between spatial locations (and can therefore be difficult to model and compute). However, similar models can be developed more conveniently in Fourier transformed space as a large set of independent processes. The originally complex high-dimensional estimation
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  Statistical methods to evaluate the effectiveness of cancer treatments in clinical trials

  Denis Rustand, Postdoctoral Research Fellow, Statistics

  Oct 14, 12:00 - 13:00

  KAUST

  Assessing the effectiveness of cancer treatments in clinical trials raises multiple methodological challenges that need to be properly addressed in order to produce a reliable estimate of treatment effects.
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  Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

  Nikolas Kantas, Associate Professor, Department of Mathematics, Imperial College London

  Oct 13, 15:30 - 16:30

  KAUST

  parameter estimation stochastic differential equations stochastic

  We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751
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  Theory and Application of Neural Networks III

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

  Oct 13, 09:00 - 10:00

  B9 L2 R2322

  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.
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  Theory and Application of Neural Networks II

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

  Oct 12, 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.
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  Asymmetric Signaling: A New Dimension of Interference Management in Hardware Impaired Communication Systems

  Sidrah Javed, Ph.D., Electrical and Computer Engineering

  Oct 11, 17:00 - 18:00

  KAUST

  Hardware impairments (HWIs) impose a huge challenge on modern wireless communication systems owing to the characteristics like compactness, least complexity, cost effectiveness and high energy efficiency. Numerous techniques are implemented to minimize the detrimental effects of these HWIs ,however, the residual HWIs may still appear as an additive distortion, multiplicative interference, or an aggregate of both. Numerous studies have commenced efforts to model one or the other forms of hardware impairments in the radio frequency (RF) transceivers. Many presented the widely linear model for in-phase and quadrature imbalance (IQI) but failed to recognize the impropriety induced in the system because of the self-interfering signals. Therefore, we have presented not only a rigorous aggregate impairment model along with its complete impropriety statistical characterization but also the appropriate performance analysis to quantify their degradation effects. Latest advances have endorsed the superiority of incorporating more generalized impropriety phenomenon as opposed to conventional propriety.
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  Close-form Solutions of A Dynamic Monetary Model

  Jenny Xiaoe Li, Associate Professor, Economics and Mathematics, Penn State University, Pennsylvania

  Oct 11, 14:00 - 15:00

  B4 B5 A0215

  This talk is devoted to the study of a monetary model proposed by Rotemberg (Journal of Political Economy, 1984). Rotemberg’s model provided a general dynamic structure for investigating government intervention in the open market operation and it has inspired the development of many other models in related fields. This talk concerns a very basic theoretical question on the model, namely the existence and the uniqueness of the equilibrium, which has been an open problem since the publication of Rotemberg’s original paper. This question was partially addressed by Rotemberg by analyzing the linearization of the equation which governs the equilibrium and obtaining a numerical solution around the steady state equilibrium.
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  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.
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  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.

Oct 3 - Oct 17, 2021

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  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 10, 2021

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  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.
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  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.
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  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.

Sep 26 - Oct 10, 2021

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  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 3, 2021

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  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.
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  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.
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  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.
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  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.
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  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.

Sep 19 - Oct 3, 2021

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  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 26, 2021

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  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.
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  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.

Sep 12 - Sep 26, 2021

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  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 19, 2021

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  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.
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  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.
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  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.
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  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.

Sep 5 - Sep 19, 2021

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  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 12, 2021

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  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.
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  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.
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  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.