Events

Jan 23 - Jan 29, 2022

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  RobotoKAUST 2022 Call for Poster Abstracts

  Prof. Eric Feron, Electrical and Computer Engineering

  Jan 25, 12:00 - Jan 31, 17:00

  KAUST

  RobotoKAUST

  We are excited to announce the KAUST Research Conference on Robotics and Autonomy 2022, which will be held on Feb 28 - March 2, 2022 (#RobotoKAUST). The conference will address the most recent trends of robotics application in a range of disciplines. The conference will give space for presentations and discussions of systems, items, and methods related to robotics and autonomy. As part of this event, we are accepting poster abstract submissions.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-01-24

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  Jan 24, 12:00 - 13:00

  KAUST

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones.
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  Towards Compact Grid Interactive EV Charging

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Jan 23, 12:00 - 13:00

  KAUST

  Towards Compact Grid Interactive EV Charging

  After a quick overview of the ECE Graduate Seminar logistics, I will share the progression of our team’s work in the field of compact integrated on-board EV charging.

Jan 16 - Jan 22, 2022

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  Miniaturized Drug Delivery Systems for Biomedical Applications

  Khalil Moussi, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Jan 18, 18:15 - 20:00

  KAUST

  Highly integrated and customizable systems have been a principal focus of development for parenteral and oral drug administration. Extensive work has been done to optimize drug efficacy via localized delivery and dosage control providing new ways for accomplishing targeted therapeutic effects. However, many challenges and opportunities for advancement remain. One promising research path is introducing novel microfabrication methods or engineering discoveries in concept realization, making devices more versatile and effective.

Jan 9 - Jan 15, 2022

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  Multilevel Methods for Stochastic Forward and Inverse Problems

  Marco Ballesio, Ph.D., Applied Mathematics and Computational Science

  Jan 12, 11:00 - 12:00

  KAUST

  This thesis studies novel and efficient computational sampling methods for applications in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation.

Dec 19 - Dec 25, 2021

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  Control Design for Alignment Problem in Optical Wireless Communication

  Asem Al Alwan , Ph.D. Student, Electrical and Computer Engineering

  Dec 23, 16:00 - 18:00

  KAUST

  Optical Wireless Communication (OWC) offers many benefits over established radio frequency-based communication links. In particular, high beam directivity generates efficient power usage and high-speed data services. Moreover, due to its ease of deployment, high transmission security, license-free operation, and insensitivity to interference, the OWC link is becoming an attractive solution for the next generation of communication systems.

Dec 12 - Dec 18, 2021

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  Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination

  Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech)

  Dec 16, 14:00 - 15:00

  B1 L3 R3119

  Indoor localization Ultrasonic-based Localization Signal processing

  High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.

Dec 5 - Dec 11, 2021

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  Backward Error Analysis for Nonlinear Differential Equations

  David Ketcheson, Professor, Applied Mathematics and Computational Science

  Dec 9, 12:00 - 13:00

  KAUST

  Backward error analysis is a tool that allows us to understand the effect of errors without knowing exact solutions.
• 

  Deconfounding

  Professor Peter Bühlmann, Statistics and Mathematics, ETH Zürich

  Dec 7, 15:30 - 16:30

  B9 L2 H2

  Hidden confounding is a severe problem when interpreting regression or causal parameters, and it may also lead to poor generalization performance for prediction. Adjusting for unobserved confounding is important but challenging when based on observational data only.
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  Statistical Learning: Causal-oriented and Robust

  Professor Peter Bühlmann, Statistics and Mathematics, ETH Zürich

  Dec 7, 12:00 - 13:00

  B9 L2 H2

  Reliable, robust and interpretable machine learning is a big emerging theme in data science and artificial intelligence, complementing the development of pure black box prediction algorithms. Looking through the lens of statistical causality and exploiting a probabilistic invariance property opens up new paths and opportunities for enhanced interpretation, robustness and external validity, with wide-ranging prospects for various applications.
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  Deploying DNNs in the Embedded Space: Challenges and Opportunities

  Christos-Savvas Bouganis, Reader in Intelligent Digital Systems in the Department of Electrical and Electronic Engineering, Imperial College London, UK

  Dec 6, 12:00 - 13:00

  B9 R2322 H1

  The talk will discuss the challenging problem of designing Deep Neural Network systems that achieve high performance under low power envelopes, hindering their deployment in the embedded space.
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  Integrated Sensors: The synergistic combination of materials and microelectronics - 2021

  Khaled Salama, Professor, Electrical and Computer Engineering

  Dec 5, 12:00 - 13:00

  KAUST

  Integrated Sensors

  In this talk we will present the design and implementation of monolithic and hybrid sensors using integrated circuits.

Nov 28 - Dec 4, 2021

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  Particle filter in high dimensions

  Dec 2, 12:00 - 13:00

  KAUST

  Filtering (or data assimilation) is the process of combining noisy observational data with an output of a numerical model to produce an optimal estimate of the evolving state of the system.
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  From Intrusion Tolerance to Trust and Recovery: A Research Overview

  Professor Miguel Correia, Computer Science and Engineering Department, Instituto Superior Técnico, Universidade de Lisboa

  Dec 1, 12:00 - 13:00

  B9 L3 R3223

  The seminar is an overview on my research path and approach. I will start with my initial research on the concept of intrusion tolerance and present some of the results I had at the time. Then, I will show how this initial research had led me to other areas and related approaches: software security, trusted computing, intrusion detection, intrusion recovery, and blockchain interoperability. I will present with some detail very recent research on securely moving data between trusted execution environments (TEEs) in a non-interactive way, a problem that must be solved for digital id crypto wallets starting to appear in Europe and elsewhere.
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  Robust high temperature operation of quantum dot lasers grown on (001) Si for Photonic Integrated Circuit

  Chen Shang, PhD candidate, University of California, Santa Barbara

  Dec 1, 08:00 - 09:00

  KAUST

  Epitaxially grown quantum dot (QD) lasers are emerging as an economical approach to obtain on-chip light sources. Thanks to the three-dimensional confinement of carriers, QDs show greatly improved tolerance to defects and promise other advantages such as low transparency current density, high temperature operation, isolator-free operation, and enhanced four-wave-mixing.
• 

  DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning

  Panagiotis Kalnis, Professor, Computer Science

  Nov 29, 12:00 - 13:00

  B9 R2322 H1

  Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification process. Most communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead.
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  Securing 5G and Beyond Networks from Malware Epidemics: A Percolation Theoretic Approach

  Hesham Elsawy, Assistant Professor, Electrical and Computer Engineering KFUPM, Dhahran

  Nov 28, 13:00 - 14:00

  B1 L3 R3119

  5G networks malware

  This talk will introduce and motivate a percolation theoretic approach to characterize and thwart malware diffusion in dense wireless networks. First, a brief tutorial on percolation theory will be introduced. Then, the talk will explain the utilization of percolation theory to develop security countermeasures for malware epidemics in 5G and beyond networks.
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  Exploiting Spatial-Temporal Graph Structure in the Data for Better EEG Source Imaging

  Feng Liu, Assistant Professor, School of Systems and Enterprises at Stevens Institute of Technology

  Nov 28, 12:00 - 13:00

  KAUST

  Exploiting Spatial

  The electroencephalogram (EEG) is an important neuroimaging modality to measure neuronal activity of the human brain.
• 

  Secure Dynamic Skyline Queries Using Result Materialization

  Gabriel Ghinita, Associate Professor, University of Massachusetts, Boston

  Nov 28, 12:00 - 13:00

  B9 L2 R2322

  Skyline computation is an increasingly popular query, with broad applicability to many domains. Given the trend to outsource databases, and due to the sensitive nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets.
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  Semiparametric Regression Models for Between and Within-subject Attributes: Applications to High-Dimensional Data, Asymptotic Efficiency and Beyond

  Jinyuan Liu, PhD Student, Biostatistics, University of California, USA

  Nov 28, 08:30 - 09:30

  KAUST

  Breakthroughs such as high-throughput sequencing are generating flourishing high-dimensional data that provoke challenges in both statistical analyses and interpretations.

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 Science

  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.
• 

  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.
• 

  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.
• 

  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 (former), Applied Mathematics and Computational Science

  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, Applied Mathematics and Computational Science

  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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  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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  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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  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 (former), 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 Science

  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 Science

  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.