Events

Apr 3 - Apr 9, 2022

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  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).
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  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.
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  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
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  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.
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  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.
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  Underwater optical communication and sensing technology in silent ocean

  Yujian Guo, Ph.D. Student, Electrical and Computer Engineering

  Mar 20, 13:00 - 15:00

  KAUST

  underwater optical communications Underwater wireless optical communication

  This dissertation aims to investigate and address noisy ocean issues and build large-scale underwater sensor networks by optical communication and sensing technology. The dissertation proposes using UWOC and FC&S technology to replace the conventional acoustic communication technology and reduce the noise in the ocean. UWOC helps achieve high-speed wireless communications between sensors, vehicles, and even humans for UIoT. The significant challenges of developing UWOC systems are the complex underwater environment's attenuation, scattering, and turbulence effects. This dissertation studied the turbulence effects on the UWOC system’s performance and addressed the pointing-acquisition-and-tracking issues.
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  Cognition-on-Chip

  Mar 20, 12:00 - 13:00

  B9 L2 R2322 H1

  As we grow, expand and experience digital world, we feel the need to interface better with the natural world which is analog.

Mar 13 - Mar 19, 2022

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  The Maxwell-Stefan system for multi-component diffusion

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Sciences

  Mar 17, 12:00 - 13:00

  B9 L2 H2

  The Maxwell-Stefan system is a system of equations commonly used to describe diffusion processes of multi-component systems. In this talk (i) I will describe modeling of multi-component systems, which leads to extensions of the Euler compressible dynamics system with mass and thermal diffusion. (ii) Will describe how the Maxwell-Stefan system emerges in the high-friction limit of multi-component Euler flows. (iii) Discuss some mathematical questions that this model raises and on the construction of numerical schemes for the Maxwell-Stefan system associated with the minimization of frictional dissipation.
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  Making Autonomous Driving more Reliable using Live 3D Digital Twins

  Fawad Ahmad, PhD Candidate, Computer Science Department, University of Southern California

  Mar 14, 18:30 - 19:30

  KAUST

  A live digital twin is a high-fidelity 3D representation of a physical object. This digital representation continuously replicates the physical object in real-time. My research vision is to build a live digital twin of the entire world. A live digital twin creates unprecedented capabilities for both computer and human consumption. It has the potential to improve safety and efficiency for autonomous driving, monitor on-going construction, and enable timely disaster relief operations etc. For humans, it means the possibility of digitally transporting to any place on the globe to live, interact and experience it in 3D like never before. These capabilities have strict performance and accuracy requirements. Achieving these requirements is not possible today for two reasons: limited wireless bandwidths, and limited on-board compute resources. I will talk about how I have tackled these challenges in my research to build end-to-end systems that build live digital twins and consume them for safer and more reliable autonomous driving. I will also discuss how I plan to implement my vision for building a live digital twin of the world in future.
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  Towards Secure and Scalable Blockchains

  Jiangshan Yu, Senior Lecturer, Monash University, Australia

  Mar 13, 12:00 - 13:00

  B2 B3 A0215

  blockchain

  Over the last decade, we have witnessed a rapid growth of blockchain technologies and their applications. Different governments have announced their strategy to boost the blockchain industry. For example, Chinese President Xi Jinping has recently endorsed blockchain and its potential for the Chinese economy; and the Australian Government announced that it would establish a National Blockchain Roadmap to help position Australia’s blockchain industry to become a global leader. This talk presents an overview of three of our works towards secure and scalable blockchains. I will first revisit the honest majority assumption of permissionless blockchains (AsiaCCS’21), and then present our efforts in making blockchain more scalable and secure against real-world threats.
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  Resilient operation of future low-carbon cyber-physical power grids

  Fei Teng, Lecturer, Electrical and Electronic Engineering

  Mar 13, 12:00 - 13:00

  B9 L2 R2322 H1

  resilient cyber-physical

  Intelligent monitoring, communication and control have been widely used in power systems to facilitate the cost-effective integration of renewable energy to achieve decarbonization.
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  Rising Stars in AI Symposium 2022 at KAUST

  Jürgen Schmidhuber, Professor, Computer Science

  Mar 13, 09:00 - Mar 15, 17:00

  B4 B5 A0215

  artificial intelligence machine learning mathematical optimization Deep Reinforcement Learning Computer Vision Natural Language Processing

  Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Mar 6 - Mar 12, 2022

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  Some Variational Problem in Morphoelasticity

  Ulisse Stefanelli, Professor, Chair of Applied Mathematics and Modeling University of Vienna

  Mar 10, 17:00 - 18:00

  KAUST

  I will present some recent work in collaboration with Elisa Davoli (TU Wien) and Katerina Nik (University of Vienna) on a three-dimensional quasistatic morpholelastic model. The mechanical response of the body and its growth are modeled by the interplay of hyperelastic energy minimization and growth dynamics. An existence result is obtained by regularization and time-discretization, also taking advantage of an exponential-update scheme. Then, we allow the growth dynamics to depend on an additional scalar field describing a nutrient, and formulate an optimal control problem. Eventually, we tackle the existence of coupled morphoelastic and nutrient solutions, when the latter is allowed to diffuse and interact with the growing body. The preprint is available as arXiv:2110.05566.
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  Machine learning assisted forward and inverse design modeling

  Waqas Waseem Ahmed, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Mar 10, 12:00 - 13:00

  KAUST

  In recent years, machine learning has proven to be efficient in solving various physical problems through data-driven approaches. For example, in wave physics, models based on analytical and numerical schemes employ intensive trial-and-error tuning of material (and geometrical) parameters for 'on demand' wave properties, which require deep understanding of the physics and are computationally expensive. As a result, it is desired to develop intelligent models that learn the bidirectional mapping between different physical quantities and automate technological device design. In this presentation, I will discuss novel generative models for forward and inverse predictions that outperform human performance. In particular, I will show how machine learning can be used to design broadband acoustic cloaks, unidirectional non-Hermitian structures, and for solving the inverse scattering problem of shape recognition.
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  The conditional particle filter - scalable MCMC for dynamic models

  Prof. Matti Vihola, Professor of Statistics, University of Jyväskylä, Finland

  Mar 9, 15:00 - 16:00

  KAUST

  This talk focuses on a 'particle MCMC' method known as the conditional particle filter (CPF), or the particle Gibbs. The CPF is a slight algorithmic variant of the original particle filter, but serves a different purpose: it defines an MCMC transition targeting the HMM smoothing distribution. The empirical evidence suggests that certain variants of the CPF mix well even in high dimensions (with long observation records). We review some theoretical insights that consolidate such empirical findings, and justify why the CPF is often efficient for HMM inference.
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  Coupled Sampling Methods for Filtering

  Fangyuan Yu, Ph.D. Student, Statistics

  Mar 7, 15:00 - 17:00

  KAUST

  Monte carlo methods computational statistics Markov models

  This thesis focuses on the use of multilevel Monte Carlo methods to achieve optimal error versus cost performance for statistical computations in hidden Markov models as well as for unbiased estimation under four cases: nonlinear filtering, unbiased filtering, unbiased estimation of hessian, continuous linear Gaussian filtering.

Feb 27 - Mar 5, 2022

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-03-03

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Mar 3, 12:00 - 13:00

  B9 L2 H2

  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. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects, (iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria. The considered applications include optimization of decision trees and decision rule systems as algorithms for problem-solving, as ways for knowledge representation, and as classifiers, optimization of element partition trees for rectangular meshes which are used in finite element methods for solving PDEs, and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths.
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  An Overview of 6G Requirements and Potential Enabling Technologies – A Mobile Network Operator's Perspective

  Dr. Youssouf Ould Cheikh Mouhamedou, Technology Alliances and Partnership Supervisor, Saudi Telecom Company (STC)

  Mar 2, 13:30 - 15:30

  B1 L3 R3119

  telecommunication 6G Cellular Networks

  The success of 5G from business perspective (creation of revenue and growth) will have a big say on the future shape of 6G. In fact, 5G standard is still evolving with new 3GPP releases issued periodically, leading to 5G-Advanced and possibly 5G-Advanced Pro in coming years. Also, 5G ecosystem is still at a relatively early stage of development. However, all major players (researchers, vendors, mobile network operators, regulators) have already started their 6G activities in line with the tradition of having G+ every 10 years. This talk is about 6G, taking into consideration mobile network operator's point of view in terms of practical and economic issues. It will provide an overview of mobile networks evolution, from 1G to 6G. It will also discuss the gap between 5G vision and 5G reality and the reasons behind it. Further, 6G key requirements as well as a tentative roadmap and timeline will be presented. Finally, some potential enabling technologies will be discussed.
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  Towards Robotic Spatial Awareness with Unsupervised Lifelong Learning

  Chen Wang, Project Scientist, Robotics Institute, Carnegie Mellon University (CMU)

  Feb 28, 14:30 - 15:30

  KAUST

  People have used robots for more than six decades to empower people to do things that are typically dirty, dull, or dangerous. The industry has also progressed significantly over the period from basic mechanical assist systems to autonomous cars, environmental monitoring, and exploration of outer space. Despite those achievements, the robots still cannot work like human being because of lacking real spatial awareness. For instance, humans can easily tidy up a messy room, while robots even have difficulty to re-identify a novel toy misplaced. To solve this problem, we need the robot to have human-like knowledge and spatial awareness, which refers to being aware of our surroundings and our state relative to them. Spatial perception consists of two processes, the exteroceptive process, which creates representations about space through feelings and learning and interoceptive process, which creates a mechanism to memorize the experience as well as cognizing information about our body, such as position and orientation. In this talk, I will preset our recent work towards robotic spatial awareness, including robust space representation, visual memorability, lifelong learning ability, and self-motion cognition. We expect that the ability of spatial awareness will be able to promote the robots to automatically adjust to stochastic, dynamic, and non-stationary environments.
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  Filling the Resources Gap with Approximate Mobile Computing

  Veljko Pejović, Assistant professor at the Faculty of Computer and Information Science (UL FRI), University of Ljubljana, Slovenia

  Feb 28, 12:00 - 13:00

  B9 R2322 H1

  Mobile computing proliferation is critically threatened by the breakdown of Dennard scaling, a law describing the area-proportional growth of integrated circuit power use.
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  The KAUST Research Conference on Robotics and Autonomy 2022

  Eric Feron, Professor, Electrical and Computer Engineering

  Feb 28, 08:00 - Mar 2, 20:00

  B19 H1

  robotics

  KAUST Robotics, Intelligent Systems and Control Lab (RISC Lab) will host the KAUST Research Conference on Robotics and Autonomy 2022 (#RobotoKAUST) from February 28 until March 2, 2022. The conference will address the most recent trends of robotics application in a range of disciplines. To attend RobotoKAUST Gala, please, read more about the event and follow the event registration instructions.
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  Modelling waves with structure-preserving numerical discretizations

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Feb 27, 14:00 - 16:00

  B9 L2 H2

  Models of physical phenomena include important qualitative properties, and any useful approximate solution of the model must respect these properties. Such properties include conservation or dissipation of energy, as well as positivity of quantities like mass, probability, or concentration. Preservation of these properties in computationally affordable numerical solutions of complex physical models remains a major challenge today. I will describe some recent advances in numerical methods for general dynamical systems that enable preservation of system dynamics and of bounds on the state, in the context of high-order accurate and efficient discretizations. The power of these methods will be demonstrated through applications in the area of surface water waves.
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  Drilling Mechanics: Bit Sensing and Physics-Based Modelling for Drilling Optimization

  Alexis Koulidis, Ph.D. Student, Physical Science and Engineering

  Feb 27, 12:00 - 13:00

  B9 L2 R2322 H1

  Drilling Mechanics

  Challenging drilling applications and fluctuating oil prices have created a new emphasis on developing innovative technology to enhance safety and reduce cost. Polycrystalline Diamond Compact (PDC) drill bits were introduced in the late 1970s and revolutionized the drilling industry, with currently 60% of the drill bits utilized in oil and gas wells being PDC.

Feb 20 - Feb 26, 2022

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  Combined analysis of spatially misaligned data using Gaussian fields and the stochastic partial differential equation approach

  Feb 24, 12:00 - 13:00

  B9 L2 H2

  Spatially misaligned data are becoming increasingly common due to advances in both data collection and management in a wide range of scientific disciplines including the epidemiological, ecological and environmental fields. Here, we present a Bayesian geostatistical model for fusion of data obtained at point and areal resolutions. The model assumes that underlying all observations there is a spatially continuous variable that can be modeled using a Gaussian random field process.
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  Computer Algebra in Scientific Computing

  Dmitry Lyakhov, Research Scientist, Applied Mathematics and Computational Sciences

  Feb 21, 12:00 - 13:00

  B9 R2322 H1

  In this talk I will speak about some recent theoretical advances in symbolic computation. In particular, exact computations with polynomials and differential equations will be discussed.
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  Extreme Statistics: Theory and Methods for Modeling Rare High-Impact Events in Complex Settings

  Raphaël Huser, Associate Professor, Statistics

  Feb 20, 16:00 - 17:30

  B4 B5 A0215

  Rare, low-probability events often lead to the biggest impacts. The goal of the Extreme Statistics (extSTAT) research group at KAUST is to develop cutting-edge statistical approaches for modeling, predicting and quantifying risks associated with these extreme events in complex systems arising in various scientific fields, such as climate science and finance. In particular, the work that we develop and continue to refine has a direct potential impact to climate scientists and related stakeholders, such as engineers and insurers, who have realized that under climate change, the greatest environmental, ecological, and infrastructural risks and damages, are often caused by changes in the intensity, frequency, spatial extent, and persistence of extreme events, rather than changes in their average behavior. However, while datasets are often massive in modern day applications, extreme events are always scarce by nature. This makes it very challenging to provide reliable risk assessment and prediction, especially when extrapolation to yet-unseen levels is required. To overcome these existing limitations, the extSTAT research group develops novel methods that transcend classical extreme-value theory to build new resilient statistical models, as well as computationally efficient inference methods, which improve the prediction of rare events in complex, high-dimensional, spatio-temporal, non-stationary settings. In this talk, I will provide an overview of my group's recent research activities and future directions with a focus on core statistical methodology contributions. The technical part of the talk will describe selected research highlights, which include (but are not limited to) the development of new flexible sub-asymptotic models applied to assessing contagion risk among leading cryptocurrencies, the development of a novel low-rank spatial modeling framework applied to estimating extreme hotspots in high-resolution Red Sea surface temperature data, and the development specialized spatio-temporal point process models applied to predicting devastating rainfall-induced landslides in a region of Italy. I will conclude my talk with an outlook on my future research plans. Motivated by methodological obstacles that arise with “big models” for complex extremes data, as well as new substantive challenges in collaborative work at KAUST, we will embark on the development of fundamentally superior models that have an intrinsically sparse probabilistic structure, as well as new "hybrid" methods that combine the strength of (parametric) models from extreme-value theory with the pragmatism and predictive power of (nonparametric) machine learning algorithms, thus opening the door to interpretable and “extreme-ly” accurate predictive models for rare events in unprecedented dimensions.
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  When Second Order Methods Shine: Big Batches, Bayes, and Bilevel

  Guodong Zhang, PhD student in University of Toronto

  Feb 20, 15:00 - 16:00

  KAUST

  In this talk, I will discuss how the use of second-order information – e.g, curvature or covariance – can help in all three problems, yet with vastly different roles in each. First, I will present a noisy quadratic model, which qualitatively predicts scaling properties of a variety of optimizers and in particular suggests that second-order optimization algorithms would extend perfect scaling to much bigger batches. Second, I will show how we can derive and implement scalable and flexible Bayesian inference algorithms from standard second-order optimization algorithms. Third, I will describe a novel second-order algorithm that finds desired equilibria and save us from converging to spurious fixed points in two-player sequential games (i.e. bilevel optimization) or even more general settings. Finally, I will conclude how my research would pave the way towards intelligent machines that can learn from data and experience efficiently, reason about their own decisions, and act in our interests.
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  Multi-physics Simulation of Plasmonic Photoconductive Devices and Antennas

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

  Feb 20, 12:00 - 13:00

  B9 L2 R2322 H1

  Multiphysics Simulation

  Terahertz (THz) frequency electromagnetic fields have numerous applications ranging from wireless communications to imaging systems and nondestructive testing, to material characterization. One of the main obstacles in the way of widespread industrial use of THz technologies is the difficulty of implementing compact and frequency-stable THz sources that can operate at room temperatures. Among a variety of possible options, photoconductive devices (PCDs) satisfy these conditions. Indeed, they have become one of the most promising candidates for THz source generation since recent advances in fabrication techniques, such as metasurface integration and nanostructured surface inclusions have significantly increased their optical-to-THz conversion efficiency and made them polarization insensitive.

Feb 13 - Feb 19, 2022

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  Modelling and Simulation of Biological Systems - 2022-02-17

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Feb 17, 12:00 - 13:00

  B9 L2 H2

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.