Events

Nov 21 - Nov 27, 2021

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  An efficient high-order reconstructions multidimensional scheme with application to instabilities in direct initiation of gaseous detonations in free space

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Sciences

  Nov 25, 12:00 - 13:00

  KAUST

  When constructing high-order schemes for solving hyperbolic conservation laws with multi-dimensional finite volume schemes, the corresponding high-order reconstructions are commonly performed in characteristic spaces to eliminate spurious oscillations as much as possible.
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  KAUST Saudi National Kidney Paired Donation Workshop

  Nov 25, 09:00 - 16:30

  B3 L5 R5220

  The 2016 Global Burden of Disease Study identifies chronic kidney disease (CKD) as the 11th most common cause of death globally, accounting for 2.17 % of deaths globally, and the 4th most common cause of death in the KSA, accounting for 4.04% of death. The preferred treatment for end-stage renal disease is kidney transplantation. Transplantation is more effective and more cost-effective than dialysis. Unfortunately, living transplant kidney donors are often incompatible with their intended recipients. Kidney Paired Donation programs circumvent these barriers as they enable patients to exchange donors. We aim to build the analytic ground for the Saudi National Paired Donation Program providing a better quality of life for the patients and net savings. This workshop brings together Saudi clinicians and KAUST scientists to discuss modern developments in Kidney Paired Donation along with strategies and research directions for Saudi National Kidney Paired Donation Program.
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  Is probability theory the only viable solution to modelling statistical information?

  Prof. Jeremie Houssineau, Department of Statistics, University of Warwick.

  Nov 24, 15:00 - 16:00

  KAUST

  probability theory data assimilation

  Many alternatives to the probabilistic modelling of information have been proposed since the birth of modern Statistics; yet, few have been successfully applied to the complex inference problems that modern Statisticians are faced with.
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  High Agility and other Aerial Robots

  Eric Feron, Professor of Electrical and Computer Engineering, KAUST, Saudi Arabia

  Nov 23, 16:30 - 17:30

  KAUST

  Abstract Robotic flight is as old as aviation itself. It might be argued that today, the only robots to whom humans are OK trusting their lives are Airplanes, Air Trains, Elevators, and Escalators. Flying Robots can take many shapes, motions, and missions. Flying robots where dynamics matters constitute the core of this presentation. To begin with, the aerobatic flight will be discussed as it happened at a time when computer power was limited and flying machines were still running with gas engines. Then, atmospheric reduced-gravity autonomous test platforms will follow, introducing the initial
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  Knowledge-based methods in bioinformatics

  Robert Hoehndorf, Associate Professor, Computer Science

  Nov 22, 12:00 - 13:00

  B9 R2322 H1

  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 identification of gene-disease associations, genomic variants that are causative for heritable disorders, and to predict protein functions. The methods I describe are generic and can be applied in other domains in which biomedical ontologies and structured knowledge bases exist.
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  Harnessing complexity for technological applications

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Nov 21, 12:00 - 13:00

  KAUST

  Harnessing complexity for technological applications

  This talk will give an overview of research activity of engineering different types of complex systems into technological applications in energy harvesting, material science, artificial intelligence and bio-imaging.

Nov 14 - Nov 20, 2021

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  Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks

  Huang Huang, Ph.D., Statistics

  Nov 18, 12:00 - 13:00

  KAUST

  Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid.
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  The Role and Importance of Sparse Matrices in Statistics

  Haavard Rue, Program Chair, Statistics

  Nov 18, 09:50 - 10:30

  KAUST

  Plenary Sessions

  Abstract The common tool for dealing with dependence in statistics, is linear dependence described through covariance or it scaled version, correlation. In this framework, a zero in the correlation matrix indicate independence, which is a very strong property and not something we would aim for in statistical models. A weaker version of linear dependence, is conditional independence. This corresponds to the inverse correlation matrix called the precision matrix. A zero in the precision matrix corresponds to conditional independence. These matrices can be very sparse although all random
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  Double Enriched Finite Volume Spaces for the DNS of fluid-Particle Interaction - Scientific Conference

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Nov 17, 14:00 - 14:40

  KAUST

  Plenary Sessions

  Abstract We present double enriched finite volume spaces for the simulation of free particles in a fluid. This involves forces being exchanged between the particles and the fluid at the interface. In an earlier work we derived a monolithic scheme which includes the interaction forces into the Navier-Stokes equations by direct coupling. In multiphase flows oscillations and spurious velocities are a common issue. The surface force term yields a jump in the pressure and therefore the oscillations are usually resolved by extending the spaces on cut elements in order to resolve the discontinuity
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  Discrete geometric structures for architecture and and computational fabrication

  Nov 15, 12:00 - 13:00

  B9 R2322 H1

  ‌ Abstract Discrete differential geometry aims to develop discrete equivalents of notions and methods from smooth surface theory. It constitutes an area of active research in mathematics, but also provides effective methodology for a variety of applications. This talk will illustrate the fruitful interplay between theory and applications of discrete differential geometry at hand of selected topics in architectural geometry and computational fabrication. We discuss the construction of architectural freeform skins from flat, nearly rectangular panels, and associated support structures. These
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  Towards Secure and Resilient Cyber-Physical Energy Systems - 2021-11-14

  Charalambos (Harrys) Konstantinou, Associate Professor, Electrical and Computer Engineering

  Nov 14, 12:00 - 13:00

  KAUST

  This talk will give an overview of the research of the Secure Next Generation Resilient Systems (SENTRY) lab (sentry.kaust.edu.sa) at KAUST.
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  Heterogeneity in Hardware: Opportunities and Challenges for Software and Applications (SC21 Panel)

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

  Nov 14, 00:05 - Nov 19, 23:55

  SC21 Saint Louis MO USA

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

  Heterogeneity in Hardware: Opportunities and Challenges for Software and Applications SC21 Panel Time: Tuesday, 16 November 2021, 10:30am - 12pm CST Location: 225-226 (America's Center, St. Louis, MO USA) Abstract With the end of Moore’s Law, the community has witnessed new hardware trends to increase performance. Today, it is not only the traditional x86 and accelerators that are part of computing systems, but also ARM, FPGAs and dedicated processors for DL workloads that equip now pioneering HPC systems. By the end of this decade, we are moving towards an era of extreme scale with “extreme

Nov 7 - Nov 13, 2021

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  Characterization of non-conventional methods for alignment relaxation in underwater wireless optical communication systems

  Mohammed Sait, Ph.D. Student, Electrical and Computer Engineering

  Nov 11, 15:00 - 16:00

  B3 L3 R5220

  The paradigm of Internet of Underwater Things (IoUT) is expected to enable various practical applications such as environmental monitoring, underwater exploration, and disaster prevention. Supporting the concept of IoUT requires robust underwater wireless communication infrastructure. Optical wireless communication has the superiority of wide bandwidth, low latency and high data capacity over its counterparts namely, acoustic and radio frequency.
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  Lessons Learned In Building Trustworthy Systems with Trusted Execution Environments

  Valerio Schiavoni, Scientific Coordinator and Lecturer, Centre of Competence for Complex Systems and Big Data, University of Neuchâtel

  Nov 11, 12:00 - 13:00

  B9 L3 R3223

  Available as dedicated hardware components into several mobile and server-grade processors, and recently included in infrastructure-as-a-service commercial offerings by several cloud providers, TEEs allow applications with high privacy and confidentiality demands to be deployed and executed over untrusted environments, shielding data and code from compromised systems or powerful attackers. After an introduction to basic concepts for TEEs, I will survey some of our most recent contributions exploiting TEEs, including as defensive tools in the context of Federated Learning, as support to build secure cache systems for edge networks, as protection mechanisms in a med-tech/e-health context, shielding novel environments (ie, WebAssembly), and more. Finally, I will highlight some of the lessons learned and offer open perspectives, hopefully useful and inspirational to future researchers and practitioners entering this exciting area of research.
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  Compatibility conditions for finite element approximations

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

  Nov 11, 12:00 - 13:00

  KAUST

  In the classical theory of the finite element approximation of elliptic partial differential equations, based on standard Galerkin schemes, the energy norm of the error decays with the same rate of convergence as the best finite element approximation, without any additional requirements on the involved spaces.
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  Deep-Ultraviolet Optoelectronics Based on GaN Quantum Disks and Bio-Inspired Nanostructures

  Ram Chandra Subedi, Ph.D. Student, Electrical and Computer Engineering

  Nov 10, 18:00 - 19:00

  B3 L5 R5209

  Optoelectronics in the deep-ultraviolet (DUV) regime is still a growing research field that requires significant effort to understand the material properties and optimize the device structures to realize highly efficient DUV devices. Of all the wide bandgap materials, AlGaN is perhaps the most studied semiconductor to replace the environmentally hazardous mercury lamps; however, the external quantum efficiency (EQE) of AlGaN based DUV devices is insufficient to replace the existing old-fashioned mercury UV lamps.
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  Manifold Markov Chain Monte Carlo Methods for Bayesian Inference In Diffusion Models

  Prof. Alexandros Beskos, the Dept of Statistical Science, University College London (UCL)

  Nov 10, 15:00 - 16:00

  KAUST

  markov chains bayesian inference nonlinearity

  Bayesian inference for nonlinear diffusions, observed at discrete times,is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology - borrowing ideas from statistical physics and computational chemistry - for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process
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  On the Downlink Operation and Architecture Optimization of Multi-User VLC Systems

  Amr M. Abdelhady, Postdoctoral Research Fellow, Communication and Computing Systems Lab

  Nov 9, 16:00 - 18:00

  KAUST

  The limited overcrowded radio frequency spectrum compelled researchers to explore higher frequency ranges for wireless transmission such as optical frequency bands. In recent decades, visible light communications (VLC) have gained lots of research attention thanks to the abundant bandwidth they offer and the existing lighting infrastructure they utilize that consequently reduces deployment costs.
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  On Recently Developed Non-Gaussian Priors and Sampling Methods with Application to Industrial Tomography

  Prof. Lassi Roininen, Applied Mathematics, LUT University

  Nov 9, 15:00 - 16:00

  B1 L4 R4214

  Non-Gaussian spatio-temporal processes Bayesian Inversion machine learning

  We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian processes. The target is to estimate the number of experts and their parameters, and to make state estimation. For sampling, we use SMC^2. For non-Gaussian priors, we discuss Cauchy priors and the generalisation to high-order Cauchy fields and further generalisation to alpha-stable fields. For sampling, we use a selection of modern MCMC tools. Finally, we apply some of the methods and models to an industrial tomography problem on estimating log internal structure, measured at sawmills, based on X-ray, RGB camera and laser scanning.
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  Computational Challenges in Sampling and Representation of Uncertain Reaction Kinetics in Large Dimensions

  Saja Almohammadi, Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 8, 16:00 - 19:00

  B3 L5 R5220

  Constructing functional representations of the key quantities of interest (QoIs), the ignition delay time ( ign), of an uncertain ignition reaction in high dimension is our main goal. First, attention is focused on the ignition delay time of an iso-octane air mixture, using a detailed chemical mechanism with 3,811 elementary reactions. Uncertainty in all reaction rates is directly accounted for using associated uncertainty factors, assuming independent log uniform priors. A Latin hypercube sample (LHS) of the ignition delay times was first generated, and the resulting database was then exploited to assess the possibility of constructing polynomial chaos (PC) representations in terms of the canonical random variables parameterizing the uncertain rates.
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  Hybrid inorganic heterostructures and methods of fabricating p-type semiconductors for optoelectronic devices

  Jian Wei Liang, Ph.D. Student, Electrical and Computer Engineering

  Nov 8, 14:00 - 16:00

  KAUST

  Semiconductor devices based on wide-bandgap materials exhibit a higher breakdown voltage, temperature tolerance, and device stability, and lower energy loss than devices based on low-bandgap materials. Several limitations challenge the use of III-nitrides and transition metal oxides as wide-bandgap materials. This thesis proposes novel methods to surmount these issues.
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  BioAI – Facts, Fiction, and the Future

  Jesper Tegner, Professor, Computer Science, KAUST

  Nov 8, 12:00 - 13:00

  B9 R2322 H1

  The natural sciences such as biology, medicine, and chemistry are currently in a transformative stage. Progress in technologies for measuring and collecting data (sequences, images, and molecules) has exploded since the human genome project. In parallel, we have witnessed stunning advances in what can broadly be referred to as computational techniques. This includes data-driven analysis of data such as Machine learning and Artificial Intelligence. From an ML/AI standpoint, there is a renewed interest in classical” equation-based modeling, causal analysis, and generative probabilistic modeling techniques. BioAI refers to this “perfect storm” between Bio and AI.
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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 6, 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.
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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 30, 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.
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  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.
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  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.
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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.
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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.

Oct 17 - Oct 23, 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 16, 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.