Events

Apr 24 - Apr 30, 2022

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  Conditionally Gaussian dynamic models, non-linear models and multi-process models for univariate time series - Lecture 2

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 26, 14:00 - 15:30

  B1 L4 R3119

  We discuss conditionally Gaussian dynamic linear models for analysis and forecasting of univariate time series and present simulation-based methods for Bayesian filtering and smoothing within this class of models, including Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods.
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  Spatio-Temporal Statistical Models Using Integro-Differential Equations

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 25, 17:45 - 19:00

  B1 L4 R4102

  We will start by presenting the general framework of Bayesian hierarchical dynamic models (BHDM) for space-time data. Within this framework, we will consider in some detail the special case of linear dynamics. We will review MCMC estimation for conditionally linear dynamic models. We will introduce integro-differential models and give a SPDE justification that provides insights into the connections between the dynamics of the process and the properties of the kernel defining the IDE.
• 

  Dynamic linear models: Theory, computation and case studies - Lecture 1

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 25, 16:00 - 17:30

  B1 L4 R4102

  In this lecture we present an overview of dynamic linear models for analysis and forecasting of univariate time series. We will discuss principles for model building and methods for Bayesian filtering, smoothing and forecasting. We will illustrate the use of these models in several case studies arising in different applied areas including environmental sciences and neuroscience.
• 

  Private Learning with Heavy-tailed Data

  Di Wang, Assistant Professor, Computer Science

  Apr 25, 12:00 - 13:00

  B9 R2322 H1

  Differential Privacy (DP) allows for rich statistical and machine learning analysis, and is now becoming a gold standard for private data analysis. Despite the noticeable success of this theory, existing tools from DP are severely limited to regular datasets, e.g., datasets need to be or are assumed to be clean and normalized before performing DP algorithms.
• 

  Model Survival Data with high and ultrahigh dimensional predictors: feature screening and statistical inference - Lecture 2

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 24, 16:00 - 18:00

  B1 L4 R4102

  Continuing on with Lecture 1, this short course introduces various cutting-edge methods that handle survival outcome data with ultrahigh dimensional predictors, that is, when the dimension of predictors is much higher than the sample size. We will also discuss several new methods for quantifying the uncertainty of estimates in a high dimensional survival setting, a very active area in machine learning.
• 

  Applied Optimization: from Energy Systems to Trajectory Planning

  Ricardo Manuel Pinto de Lima, Research Scientist, Applied Mathematics and Computational Sciences

  Apr 24, 14:00 - 15:00

  B9 L2 H2

  This talk focus on advanced modeling and optimization techniques to solve complex problems in the scheduling and planning of processes, energy systems, and water systems. Modeling and optimization techniques integrated with other disciplines have a paramount role in supporting the energy transition planning and the water-energy nexus. Specifically, optimizing the integration of renewable energy sources and new energy carriers in the energy mix, increasing the efficiency of existing and novel systems (enterprise-wide, industry, buildings, supply chains), and supporting decarbonization in industry. First, we describe canonical optimization formulations and show their flexibility and capabilities to address complex optimization problems. Examples of applications include process synthesis, production scheduling, short-term hydro scheduling, unit commitment, and even the transfer of human resources within large organizations. Then we focus on applying stochastic optimization approaches to address a virtual power plant's self-scheduling and market involvement, and finally on applying advanced optimization strategies for trajectory planning of underwater vehicles in uncertain and transient flow fields.
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  Feedback system design for tuning cooperative behavior in multi-agent games

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Apr 24, 12:00 - 13:00

  B9 L2 R2322 H1

  Feedback system design

  In social dilemmas, a class of multi-agent games, agents' rationality-based strategic interactions to learn a payoff-maximizing strategy would result in diminishing returns. Such games include the prisoner's dilemma and public goods game where individually rational decision making reults in all decision-making agents receiving smallest rewards. In this presentation, I will explain a new decision-making model that elicits cooperative behavior in social dilemmas. The model enables the social interaction (reciprocity) between agents in their decision making, which allows cooperative behavior to emerge. We discuss how methods for feedback system design and analysis can be applied to explain the emergence of cooperative behavior and how we can tune such behavior.

Apr 17 - Apr 23, 2022

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  Gaussian Graphical Regression with High-Dimensional Responses and Covariates

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 21, 16:30 - 17:30

  B2 B3 A0215

  Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks.
• 

  Enhancing mixing in binary fluids

  Prof. Peter J. Schmid, Professor, Mechanical Engineering, KAUST

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  Mixing is a key process in a wide variety of technological applications. Pharmaceuticals, consumer products, and oil & gas derivatives, among many other products, rely on an energy-efficient, robust and effective mixing process. We will formulate the mixing process as a PDE-constrained optimization problem using special norms that concentrate on the mixedness of a passive scalar transported by nonlinear governing equations.
• 

  Energy-Efficient Devices and Circuits for Ultra-Low Power VLSI Applications

  Ren Li, PhD Student, Electrical and Computer Engineering

  Apr 20, 15:00 - 17:00

  KAUST

  In this thesis, energy-efficient Micro-/Nano-electromechanical (M/NEM) relays are introduced, their nonleaking property and abrupt switch ON/OFF characteristics are studied, designs and applications in the implementation of ultra-low-power integrated circuits and systems are explored. The proposed designs compose of core building blocks for any functional microprocessor, for instance, fundamental logic gates; arithmetic adder circuits; sequential latch, and flip-flop circuits; input/output (I/O) interface data converters, including an analog-to-digital converter (ADC) and a digital-to-analog converter (DAC); system-level power management DC-DC converters and energy management power-gating scheme. Another contribution of this thesis is the study of device non-ideality and variations on the functionality of circuits. We have thoroughly investigated energy-efficient approximate computing with non-ideal transistors and relays for the next generation of ultra-low-power VLSI systems.
• 

  Regularized Regression for Survival Data: Methods and Applications

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 19, 16:00 - 18:00

  B1 L4 R4102

  In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.
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  Electric Power to the People: Sustainable, Secure, and Resilient Energy Systems

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

  Apr 18, 12:00 - 13:00

  B9 R2322 H1

  The power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system.
• 

  Advanced ultrawide bandgap semiconductors for ultraviolet photonics

  Yi Lu, Ph.D. Student, Electrical and Computer Engineering

  Apr 17, 12:00 - 13:00

  B9 L2 R2322 H1

  advanced semiconductors

  The advancement of silicon semiconductor technology since the 1940s has been changing the world by redefining human living style and addressing global challenge of energy shortage. Since the 1960s, GaAs, InP research started to bring human beings into modern life with mobile communications and high-speed networks. When it goes to the 1980s, wide band gap semiconductors including GaN and SiC which have high breakdown field and high mobility, were demonstrated into high-power electronics as well as solid-state lighting.

Apr 10 - Apr 16, 2022

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  Biomedical research: a world of statistical challenges

  Prof. David Gomez-Cabrero, Biological, Environmental Science and Eng, KAUST

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
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  Causal aggregation: estimation and inference of causal effects by aggregating information across data sets

  Prof. Dominik Rothenhaeusler, Statistics, Stanford University

  Apr 12, 17:00 - 18:00

  KAUST

  Randomized experiments are the gold standard for causal inference. In experiments, usually, one variable is manipulated and its effect is measured on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates on a fixed target variable.
• 

  CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

  Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

  Apr 12, 13:00 - 14:00

  KAUST

  machine learning Computer science artificial intelligence

  Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.
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  Geospatial Data Science for Public Health Surveillance

  Apr 11, 12:00 - 13:00

  B9 R2322 H1

  Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors and measure inequalities. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance.
• 

  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 3

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 11, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
• 

  Approximation of Mean Field Games and related problems - Prof. Francisco Silva -Part 3

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 11, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
• 

  Six years of ActivityNet: Progress and Remaining Challenges for Temporal Activity Localization

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Apr 10, 14:00 - 16:00

  B9 L2 H2

  Recognizing and localizing human activities in long-form untrimmed video is one of the most important applications of computer vision. This remains a difficult problem given the many challenges inherent to untrimmed video ranging from its large-scale nature to the need for rich video representation and context modeling. In an effort to better understand this problem, ActivityNet was proposed in 2015 to provide a large-scale benchmark to train and evaluate activity localization methods. Since then, it has become a standard in the community, and its annual workshop at CVPR has become a popular venue for research groups to compete and present their newest approaches. Despite the advances in the past several years, the performance of activity localization methods in video remains limited, especially in comparison with human performance. In this colloquium, I will give a compact view of the progress made in the task of temporal activity localization, identify some key remaining challenges, and present some future directions.
• 

  Semiconductor Epitaxy and Micro-Device Fabrication Toward AR and VR World

  Kazuhiro Ohkawa, Professor, Electrical and Computer Engineering

  Apr 10, 12:00 - 13:00

  B9 L2 R2322 H1

  Wearable, fast-response, and efficient displays are suitable to enjoy the AR and VR world.
• 

  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini -Part 2

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 10, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
• 

  Approximation of Mean Field Games and related problems Prof. Francisco Silva -Part 2

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 10, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.

Apr 3 - Apr 9, 2022

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  Phasewise-Conservative and Bound-Preserving Algorithms for Two-Phase Flow in Porous Media

  Shuyu Sun, Professor, Earth Science and Engineering

  Apr 7, 12:00 - 13:00

  B9 L2 H2

  Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together. Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation.
• 

  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 1

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 7, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by and in order to model dynamic games with a large number of indistinguishable small players. In the model proposed in , the asymptotic equilibrium is described by means of a system of two Partial Diferential Equations (PDEs).
• 

  Approximation of Mean Field Games and related problems - Prof. Francisco Silva - Part 1

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 7, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by and in order to model dynamic games with a large number of indistinguishable small players.
• 

  Finite element approximation of Stokes equations with non-smooth data

  Lucia Gastaldi, Professor Department of Mathematics, University of Brescia

  Apr 6, 16:00 - 17:00

  B1 L4 R4102

  Finite element methods

  Finite element approximation of Stokes equations with non-smooth data
• 

  Bayesian Modeling of Sub-Asymptotic Spatial Extremes

  Rishikesh Yadav, Ph.D. Student, Statistics

  Apr 5, 15:00 - 17:00

  B5 L5 R5220

  spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

  In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.
• 

  Learning-Based Approaches for Next-Generation Intelligent Networks

  Liang Zhang, Ph.D. Student, Electrical and Computer Engineering

  Apr 5, 14:00 - 16:00

  KAUST

  communication network problems communication networks

  In this thesis, we investigate how learning-based approaches are implemented to solve the communication network problems and how communication network dependencies impact the training of learning-based approaches.
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  Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes

  Peng Zhong, Ph.D. Student, Statistics

  Apr 4, 17:00 - 19:00

  B3 L5 R5209

  The statistical modeling of extreme natural hazards is becoming increasingly important due to climate change, whose effects have been increasingly visible throughout the last decades. It is thus crucial to understand the dependence structure of rare, high-impact events over space and time for realistic risk assessment. For spatial extremes, max-stable processes have played a central role in modeling block maxima. However, the spatial tail dependence strength is persistent across quantile levels in those models, which is often not realistic in practice. This lack of flexibility implies that max-stable processes cannot capture weakening dependence at increasingly extreme levels, resulting in a drastic overestimation of joint tail risk.
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  Visualization for DNA Nanotechnology Design and Simulations

  Ivan Viola, Professor, Computer Science

  Apr 4, 12:00 - 13:00

  B9 R2322 H1

  DNA Nanotechnology is a fascinating field that studies how to construct small biological structures entirely from DNA as a building material. The key insight is that DNA, if designed in a particular way, can construct complex 3D nanoscale structures entirely by means of self-assembly, governed by the base-pairing principle.
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  Towards Miniaturized, Flexible and Wearable Wireless Communication and Sensing Systems for IoT Applications

  Atif Shamim, Program Chair, Electrical and Computer Engineering

  Apr 3, 14:00 - 16:00

  B9 L2 H2

  With the advent of wearable sensors and internet of things (IoT) applications, there is a new focus on electronics which can be compact, light-weight and flexible so that these can be worn or mounted on non-planar objects. Due to large volume (billions of devices), it is required that the cost is extremely low, to the extent that they become disposable. In the context of miniaturization and lower cost, concepts such as System-on-chip (SoC) where a complete system is realized on a single chip (integrated circuit (IC)) or System-on-package (SoP) where the package of the chip is made functional are beneficial. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet and screen printing. Two important aspects of any IoT system, “Sensing” and “Wireless Communication”, will be the focus of this talk. The SoC part of the talk will focus on integration of the antenna on the chip and ways of enhancing its efficiency despite the lossy Silicon substrate in conventional semiconductor manufacturing processes. Through a SoP design example, it will be shown how smart packaging of a chip can boost the performance without adding any additional components or cost. In the later part of the talk, additive manufacturing will be introduced as an emerging technique to realize low cost and flexible wireless communication and sensing systems. Various novel functional inks, such as conductive, dielectric, phase change and sensing materials will be shown. A multilayer process will be presented where dielectrics are also printed in addition to the metallic parts, thus demonstrating fully printed components. Finally, some printed sensor examples will be shown for remote health and environmental monitoring. The promising results of these designs indicate that the day when electronics can be printed like newspapers and magazines through roll-to-roll printing is not far away.
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  Organic Electronics for Diagnostics in Physiological Media

  Prof. Sahika Inal, Biological, Environmental Science and Engineering, KAUST

  Apr 3, 12:00 - 13:00

  B9 L2 H1

  organic electronics

  Organic mixed (ionic and electronic) charge conductors and devices offer a new toolbox for interfacing with biological systems.

Mar 27 - Apr 2, 2022

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  ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

  Peter Richtarik, Professor, Computer Science

  Mar 31, 12:00 - 13:00

  B9 L2 H2

  We introduce ProxSkip a surprisingly simple and provably efficient method for minimizing the sum of a smooth (ƒ) and an expensive nonsmooth proximable (ψ) function.
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  To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

  Zhuo Yang, Ph.D. Student, Computer Science

  Mar 30, 17:30 - 19:30

  KAUST

  machine learning data mining

  Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.
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  Random Persistence Diagram Generation

  Prof. Vasileios Maroulas, Director of AI and Data Science at the National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee Knoxville.

  Mar 30, 15:00 - 16:00

  KAUST

  Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores them in persistence diagrams (PDs). In this talk we will discuss a random persistence diagram generation (RPDG) method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned by (i) a model based on pairwise interacting point processes for inference of persistence diagrams, and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. An example on a materials science problem will demonstrate the applicability of the RPDG method.
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  High-dimensional Regression and Dictionary Learning: Some Recent Advances for Tensor Data

  Prof. Waheed Bajwa, Electrical and Computer Engineering, Rutgers University-New Brunswick

  Mar 30, 14:15 - 15:15

  B1 L3 R3119

  machine learning

  Data in many modern signal processing, machine learning, and statistics problems tend to have tensor (aka, multidimensional / multiway array) structure. While traditional approaches to processing of such data involve 'flattening' of data samples into vectors, it has long been realized that explicit exploitation of tensor structure of data can lead to improved performance. Recent years, in particular, have witnessed a flurry of research activity centered around development of computational algorithms for improved processing of tensor data.
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  Omics-based Metastasis Prediction using Machine Learning and Deep Learning

  Somayah Albaradei, Ph.D. Student, Computer Science

  Mar 30, 14:00 - 15:00

  KAUST

  bioinformatics Computational biology machine learning Computer Vision

  Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).
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  Masterclass on Visual Computing

  Dominik Michels, Helmut Pottmann, Ivan Viola, Peter Wonka, Soeren Pirk, Wolfgang Heidrich

  Mar 29, 14:30 - Mar 30, 17:15

  KAUST

  visual computing

  Visual Computing has become a key enabling technology for a diverse set of applications spanning scientific discovery, digital services, medicine, robotics, consumer electronics, and entertainment, to name just a few. The research community tackles problems in this vast space by drawing from expertise in multiple disciplines, including Computer Science, Electrical Engineering, and Mathematics. The KAUST Masterclass on Visual Computing highlights a selection of cutting-edge academic research within this field by comprising a series of talks focusing on different topics ranging from Computational Architecture and Fabrication, Deep Optics, and Generative Modeling, to Nanovisualization, Physics-based simulation, and Representation Learning.
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  Monolithic matrix-free solver for fluid-structure interaction problems

  Michał Wichrowski, IWR, University of Heidelberg (Germany)

  Mar 28, 16:00 - 17:00

  B1 L4 R4214

  monolithic

  Monolithic matrix-free solver for fluid-structure interaction problems.
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  Multifunctional Flexible Laser-Scribed Graphene Sensors for Resilient and Sustainable Electronics

  Altynay Kaidarova, Ph.D. Student, Electrical and Computer Engineering

  Mar 28, 13:00 - 15:00

  KAUST

  Laser-scribed graphene

  This dissertation introduces flexible, lightweight, and robust Laser-Scribed Graphene (LSG) sensor solutions for detecting various physical parameters, such as strain, flow, deflection, force, pressure, temperature, conductivity, and magnetic field. Multifunctionality was obtained by exploiting the direct laser scribing process combined with the flexible nature of polyimide and the piezoresistivity of porous graphene. The outstanding properties of LSG, such as low cytotoxicity, biocompatibility, corrosion resistance, and ability to function under extreme pressure and temperature conditions, allowed targeting diverse emerging applications.
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  Energy Efficient Neuromorphic computing systems

  Khaled Salama, Professor, Electrical and Computer Engineering

  Mar 28, 12:00 - 13:00

  B9 R2322 H1

  Traditional computing systems separate processors from memory, performing computation by shuttling data back and forth between these two units all the time. This bottleneck incurs limited processing speed and high power consumption in computing systems for deep learning models of ever-increasing complexity. Novel approaches and new principles are needed to revolutionize computing systems. Neuromorphic systems are proposed as a new computing architecture based on spiking neural networks analogous to the existing nervous systems.
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  Research conference "Flow Simulation at the Exascale: Opportunities, challenges and its application in the industry"

  Matteo Parsani, David Keyes, Rasha Al Jahdali, Lisandro Dalcin, Bilel Hadri, Hong Im, Ravi Samtaney, Gabriel Wittum

  Mar 28, 08:30 - Mar 30, 17:00

  KAUST Library

  We are excited to announce the KAUST Research Conference on Flow simulation at the exascale: Opportunities, challenges, and its application in the industry, which will be held on March 28 – March 30, 2022 (#ExaCFDKAUST). The conference aims to bring together experts in flow simulation, computational mathematics, and high-performance computing. The goal is to define a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing and beyond. The conference will give space for presentations and discussions of computational fluid dynamics. As part of this event, we are accepting poster abstract submissions. The poster should present high-quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of Computational Fluid Dynamics. For more information please visit the conference website.
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  The Best of Both Worlds: Distributed PCA That is Both Exact and Communication Efficient

  Prof. Waheed Bajwa, Electrical and Computer Engineering, Rutgers University-New Brunswick

  Mar 27, 12:00 - 13:00

  B9 L2 R2322 H1

  Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning.

Mar 20 - Mar 26, 2022

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  Regression models for count and proportion data

  Alexandre de Bustamante Simas , Research Scientist, Statistics

  Mar 24, 12:00 - 13:00

  B9 L2 H2

  Abstract In this talk we will present two classes of regression models. The first class is focused on overdispersed count models and is given by assuming that the response variable is obtained by mixing the Poisson distribution with an absolutely continuous distribution in the exponential family. The second class is focused on proportion data and assumes that the response variable follows a distribution given by the fraction $X/(X+Y)$, where $X$ and $Y$ are independent absolutely continuous random variables from the exponential family. Usually, the marginal likelihood of the response variables
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  Waste-free Sequential Monte Carlo

  Prof. Nicolas Chopin, Professor of Statistics, ENSAE, Paris

  Mar 23, 16:00 - 17:00

  KAUST

  A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm in practice. We develop in particular a method to estimate, from a single run of the algorithm, the asymptotic variance of any particle estimate. We show empirically, through a range of numerical examples, that waste-free SMC tends to outperform standard SMC samplers, and especially so in situations where the mixing of the considered MCMC kernels decrease across iterations (as in tempering or rare event problems).
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  The capabilities of summation-by-parts and structure-preserving operators for compressible computational fluid dynamics and reaction-diffusion models

  Mohammed Sayyari, Ph.D. Student, Applied Mathematics and Computational Sciences

  Mar 21, 14:00 - 16:00

  B2 L5 R5209

  With the algorithm's suitability for exploiting current petascale and next-generation exascale supercomputers, stable and structure-preserving properties are necessary to develop predictive computational tools. This dissertation uses the mimetic properties of SBP-SAT operators and the structure-preserving property of a new relaxation procedure for Runge--Kutta schemes to construct nonlinearly stable full discretizations for non-reactive compressible computational fluid dynamics (CFD) and reaction-diffusion models.
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  Permutation compressors for provably faster distributed nonconvex optimization

  Peter Richtarik, Professor, Computer Science

  Mar 21, 12:00 - 13:00

  B9 R2322 H1

  We study the MARINA method of Gorbunov et al (ICML 2021) - the current state-of-the-art distributed non-convex optimization method in terms of theoretical communication complexity. Theoretical superiority of this method can be largely attributed to two sources: the use of a carefully engineered biased stochastic gradient estimator, which leads to a reduction in the number of communication rounds, and the reliance on {\em independent} stochastic communication compression operators, which leads to a reduction in the number of transmitted bits within each communication round.
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  IVth KAUST MaS Workshop on Modelling and Simulation

  speakers from KAUST, Birmingham, Graz, Utrecht, Stuttgart, Frankfurt, Buffalo, Linz, Weissach, Lugano, Kaliningrad, Heidelberg, State College, Philadelphia, Torino, Riyadh

  Mar 21, 09:00 - Mar 23, 17:30

  B3 L5 R5209

  Applied Partial Differential Equations

  The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Models based on partial differential equations Novel models in biosciences Computational methods in empirical sciences. Numerical methods for partial differential equations Historical and scientific background Previous KAUST MaS workshops were held in 2017, 2018 and 2019 To download the Workshop's Agenda click here. Agenda Monday, 21 March 2022 Day 1 Saudi Arabia(AST) Speakers Description 10:00 Tony Chan (KAUST President) Opening Remarks 10:15 Gundolf Haase
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  Research conference: Extreme Bandwidth Communication: From mmWave, THz to Optical Bands

  Atif Shamim, Mohamed-Slim Alouini, Hakan Bagci

  Mar 21, 08:30 - Mar 23, 17:30

  KAUST Library and KAUST please click registration link at the bottom

  The technological evolution has led to the current high-performing wireless communication systems that we use on a daily basis. However, coping with the increasing demand is becoming more and more challenging, especially since we are approaching the limits of what can be done with the available resources. One of these resources is bandwidth. This spectrum scarcity problem has motivated researchers to explore new frequencies for wireless communications. Due to this reason, the upper radio-frequency (RF) spectrum, from mmWave and THz to optical bands, is being pursued, which is termed as “Extreme Bandwidth Communication.” This conference brings world experts and the brightest minds from academia and industry to present the latest trends, challenges, results, and opportunities in the field of extreme bandwidth communication.