Events

Jun 27 - Jul 3, 2021

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  ISC21 Workshop: Numerical Algorithms and Libraries for Exascale (NAL-X)

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

  Jul 2, 14:00 - 18:00

  ISC21 KAUST Frankfurt Germany Time CET

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

  Abstract With the hardware technology scaling and the trend on heterogeneous chip design, the existing numerical algorithms and software framework may break down due to load imbalance. There is currently a fundamental mismatch between the underlying hardware architecture with high thread concurrency and the software deployment of numerical libraries, which relies on the traditional bulk synchronous programming model. Numerical software should first squeeze performance out of single node by efficiently running on manycore architectures with processor counts sharing a common memory in the
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  State estimation for nonlinear dynamical systems: Methods and applications | Part 1C

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jun 30, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly
• 

  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 1B

  Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

  Jun 29, 14:00 - 17:00

  KAUST

  nonlinear dynamics linear matrix inequalities Lyapunov methods

  Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly
• 

  Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

  Robert Hoehndorf, Associate Professor, Computer Science

  Jun 27, 10:00 - 11:00

  B9 L2 H2

  artificial intelligence

  Abstract 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 the identification of gene-disease

Jun 13 - Jun 19, 2021

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  Reconfigurable Snapshot HDR Imaging Using Coded Masks

  Masheal Alghamdi, Ph.D., Computer Science

  Jun 17, 12:00 - 14:00

  KAUST

  High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this thesis, we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware combined with a deep learning algorithm to reconstruct the HDR image. In this approach, we achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible, yet robust, way to modulate per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparative analysis demonstrated that our method outperforms the state-of-the-art in terms of visual perception quality.
• 

  The envelope formula and its applications in variational analysis - Session 3

  Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

  Jun 16, 19:00 - 21:00

  KAUST

  I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
• 

  Learning over Graphs

  Ali H. Sayed, Dean of Engineering, EPFL Switzerland

  Jun 15, 16:30 - 17:45

  KAUST

  This talk explains how agents over a graph can learn from dispersed information and solve inference tasks of varying degrees of complexity through localized processing. The presentation also shows how information or misinformation is diffused over graphs, how beliefs are formed, and how the graph topology helps resist or enable manipulation. Examples will be considered in the context of social learning, teamwork, distributed optimization, and adversarial behavior.
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  The envelope formula and its applications in variational analysis - Session 2

  Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

  Jun 14, 19:00 - 21:00

  KAUST

  I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.

Jun 6 - Jun 12, 2021

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  An introduction to bifurcation theory and applications to MFG - Session 2

  Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy

  Jun 10, 14:00 - 17:00

  KAUST

  In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.
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  The envelope formula and its applications in variational analysis - Session 1

  Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

  Jun 9, 19:00 - 21:00

  KAUST

  I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
• 

  An introduction to bifurcation theory and applications to MFG - Session 1

  Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy

  Jun 8, 15:00 - 18:00

  KAUST

  In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.
• 

  Research on Ultrawide Bandgap Semiconductors

  Xiaohang Li, Associate Professor, Electrical and Computer Engineering

  Jun 8, 15:00 - 16:30

  KAUST

  Wide bandgap (WBG) semiconductors including GaN have demonstrated great success in lighting, display, electrification, and 5G communication due to superior properties and decades of R&D. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) semiconductors with bandgap larger than GaN have attracted increasing attentions. They are regarded as the 4th wave of the inorganic semiconductors after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, crucial for sustainability and health of the human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. This seminar would cover the latest research by the Advanced Semiconductor Lab. It includes multi-disciplinary studies of growth, materials, physics, and devices of the UWBG semiconductors.
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  Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields

  Mary Lai O. Salvana, Ph.D. Student, Statistics

  Jun 7, 17:00 - 19:00

  KAUST

  In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is difficult as these functions should yield positive definite covariance matrices. In recent years, we have seen a flourishing of methods and theories on constructing spatio-temporal covariance functions satisfying the positive definiteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions fulfills this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In this dissertation, several developments are proposed and new features are provided to this special class.

May 30 - Jun 5, 2021

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  Part 2B | PDE Backstepping Boundary Control of Parabolic PDEs

  Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

  Jun 2, 17:00 - 18:30

  KAUST

  Partial Differential Equations Lyapunov methods linear parabolic PDEs parabolic-hyperbolic PDE

  Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.
• 

  Part 2A | PDE Backstepping Boundary Control of Parabolic PDEs

  Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

  Jun 2, 15:00 - 16:30

  KAUST

  Partial Differential Equations Lyapunov methods linear parabolic PDEs parabolic-hyperbolic PDE

  Abstract Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite
• 

  Part 1B | PDE Backstepping Boundary Control of Parabolic PDEs

  Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

  Jun 1, 17:00 - 18:30

  KAUST

  Partial Differential Equations Lyapunov methods linear parabolic PDEs parabolic-hyperbolic PDE

  PDE Backstepping Boundary Control of Parabolic PDEs Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.
• 

  Stationary and Cyclostationary Processes for Time Series and Spatio-Temporal Data

  Soumya Das, Ph.D. Student, Statistics

  Jun 1, 16:00 - 18:00

  KAUST

  Due essentially to the difficulties associated with obtaining explicit forms of stationary marginal distributions of non-linear stationary processes, appropriate characterizations of such processes are worked upon little. After discussing an elaborate motivation behind this thesis and presenting preliminaries in Chapter 1, we characterize, in Chapter 2, the stationary marginal distributions of certain non-linear multivariate stationary processes. To do so, we show that the stationary marginal distributions of these processes belong to specific skew-distribution families, and for a given skew-distribution from the corresponding family, a process, with stationary marginal distribution identical to that given skew-distribution, can be found.
• 

  Part 1A | PDE Backstepping Boundary Control of Parabolic PDEs

  Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

  Jun 1, 15:00 - 16:30

  KAUST

  Partial Differential Equations Lyapunov methods linear parabolic PDEs parabolic-hyperbolic PDE

  Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.
• 

  Flexible Covariance Models for Spatio-Temporal and Multivariate Spatial Random Fields

  Ghulam A. Qadir, Ph.D. Student, Statistics

  May 31, 16:00 - 18:00

  KAUST

  The modeling of spatio-temporal and multivariate spatial random fields has been an important and growing area of research due to the increasing availability of space-time-referenced data in a large number of scientific applications. In geostatistics, the covariance function plays a crucial role in describing the spatio-temporal dependence in the data and is key to statistical modeling, inference, stochastic simulation, and prediction. Therefore, the development of flexible covariance models, which can accommodate the inherent variability of the real data, is necessary for advantageous modeling of random fields. This thesis is composed of four significant contributions in the development and applications of new covariance models for stationary multivariate spatial processes, and nonstationary spatial and spatio-temporal processes. Firstly, this thesis proposes a semiparametric approach for multivariate covariance function estimation with flexible specification of the cross-covariance functions via their spectral representations. The flexibility in the proposed cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows for capturing non-trivial cross-spectral features. The proposed method is applied to model and predict the bivariate data of particulate matter concentration and wind speed in the United States. Secondly, this thesis introduces a parametric class of multivariate covariance functions with asymmetric cross-covariance functions. The proposed covariance model is applied to analyze the asymmetry and perform prediction in a trivariate data of particulate matter concentration, wind speed and relative humidity in the United States.
 Thirdly, the thesis presents a space deformation method which imparts nonstationarity to any stationary covariance function. The proposed method utilizes the functional data registration algorithm and classical multidimensional scaling to estimate the spatial deformation. The application of the proposed method is demonstrated on precipitation data from Colorado, United States. Finally, this thesis proposes a parametric class of time-varying spatio-temporal covariance functions, which are stationary in space but nonstationary in time. The proposed time-varying spatio-temporal covariance model is applied to study the seasonality effect and perform space-time predictions in the daily particulate matter concentration data from Oregon, United States.

May 23 - May 29, 2021

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  Computational Wavefront Sensing: Theory, Practice, and Applications

  Congli Wang, Ph.D. Student, Electrical and Computer Engineering

  May 26, 18:00 - 19:30

  KAUST

  Wavefront sensing is a fundamental problem in applied optics. Wavefront sensors that work in a deterministic manner are of particular interest. Initialized with a unified theory for classical wavefront sensors, this dissertation discusses relevant properties of wavefront sensor designs. Based on which, a new wavefront sensor, termed Coded Wavefront Sensor, is proposed to leverage the advantages of the analysis, especially the lateral wavefront resolution. A prototype was built to demonstrate this new wavefront sensor.
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  Minimization problems involving polyconvex functionals and an H1-projection problem - Session 3

  Wilfrid Gangbo, Professor of mathematics at the University of California, Los Angeles

  May 25, 19:00 - 21:00

  KAUST

  We recall the state of the art and the role of polyconvexity in the calculus of variations. Then we keep our focus on a particular polyconvex function, applicable to the study of Euler incompressible fluids. We prove the existence, uniqueness, and regularity of minimizers of a polyconvex functional which corresponds to the H1-projection of measure-preserving maps. Our result introduces a new criteria on the uniqueness of the minimizer, based on the smallness of the Lagrange multiplier. No estimate on the second derivatives of the pressure is needed to get a unique global minimizer. We introduce a minimizing movement scheme to construct Lr-solutions of the Navier-Stokes equation (NSE) for a short time interval. Our scheme is an improved version of the split scheme introduced in Ebin–Marsden in 1970, and allows us to solve the equation with less regular initial data as opposed to more regular initial data requirement in the 1970 Ebin–Marsden’s work. (Most of the material of these lectures is based on a joint work with M. Jacobs and I. Kim).
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  Towards 6G Wireless Communication Networks: Vision, Enabling Technologies, and New Paradigm Shifts

  H. Vincent Poor, Michael Henry Strater University Professor, Princeton University

  May 25, 15:30 - 16:30

  KAUST

  Fifth generation (5G) wireless communication networks are being deployed worldwide and more capabilities are in the process of being standardized, such as massive connectivity, ultra-reliability, and low latency. However, 5G will not meet all requirements of the future, and sixth generation (6G) wireless networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, greater intelligence and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architectures, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. One vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication networks. Multiple spectra will be exploited to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the very large datasets generated by heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of AI-related technologies. And, fourth, network security will have to be strengthened when developing 6G networks. This talk will review recent advances and future trends in these four aspects.

May 9 - May 15, 2021

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  Minimization problems involving polyconvex functionals and an H1-projection problem - Session 2

  Wilfrid Gangbo, Professor of mathematics at the University of California, Los Angeles

  May 11, 19:00 - 21:00

  KAUST

  We recall the state of the art and the role of polyconvexity in the calculus of variations. Then we keep our focus on a particular polyconvex function, applicable to the study of Euler incompressible fluids. We prove the existence, uniqueness, and regularity of minimizers of a polyconvex functional which corresponds to the H1-projection of measure-preserving maps. Our result introduces a new criteria on the uniqueness of the minimizer, based on the smallness of the Lagrange multiplier. No estimate on the second derivatives of the pressure is needed to get a unique global minimizer. We introduce a minimizing movement scheme to construct Lr-solutions of the Navier-Stokes equation (NSE) for a short time interval. Our scheme is an improved version of the split scheme introduced in Ebin–Marsden in 1970, and allows us to solve the equation with less regular initial data as opposed to more regular initial data requirement in the 1970 Ebin–Marsden’s work. (Most of the material of these lectures is based on a joint work with M. Jacobs and I. Kim).

May 2 - May 8, 2021

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  Scalable Solvers: Universals and Innovations

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  May 6, 12:00 - 13:00

  KAUST

  Abstract As simulation and analytics enter the exascale era, numerical algorithms must span a widening gap between ambitious applications and austere architectures. We present fifteen universals for researchers in scalable solvers and some innovations that allow approaching lin-log complexity in storage and operation count in many important algorithmic kernels. Applications are becoming ambitious in many senses: large physical space, phase space or parameter dimensions; resolution of many scales of space and/or time; high fidelity physical modeling; linking together of multiple complex models
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  Minimization problems involving polyconvex functionals and an H1-projection problem - Session 1

  Wilfrid Gangbo, Professor of mathematics at the University of California, Los Angeles

  May 4, 19:00 - 21:00

  KAUST

  We recall the state of the art and the role of polyconvexity in the calculus of variations. Then we keep our focus on a particular polyconvex function, applicable to the study of Euler incompressible fluids. We prove the existence, uniqueness, and regularity of minimizers of a polyconvex functional which corresponds to the H1-projection of measure-preserving maps. Our result introduces a new criteria on the uniqueness of the minimizer, based on the smallness of the Lagrange multiplier. No estimate on the second derivatives of the pressure is needed to get a unique global minimizer. We introduce a minimizing movement scheme to construct Lr-solutions of the Navier-Stokes equation (NSE) for a short time interval. Our scheme is an improved version of the split scheme introduced in Ebin–Marsden in 1970, and allows us to solve the equation with less regular initial data as opposed to more regular initial data requirement in the 1970 Ebin–Marsden’s work. (Most of the material of these lectures is based on a joint work with M. Jacobs and I. Kim)
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  Community Detection in Biological Networks: Lessons from the DREAM 2016 Module Identification Challenge

  Lenore J. Cowen is a Professor in the Computer Science Department at Tufts University

  May 3, 18:30 - 19:30

  KAUST

  The 2016 DREAM Disease Module Identification Challenge was developed to systematically assess the state of computational module identification methods on a diverse collection of molecular networks. Six different anonymized networks were presented with the gene names anonymized. The goal was to partition the genes into non-overlapping modules of from 3-100 genes each, based soley on the patterns of network connectivity.
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  Advances in Microelectromechanical Digital Circuit Design: Low Power Computation, Sensing and Beyond

  Hossein Fariborzi, Adjunct Professor (former), Electrical and Computer Engineering

  May 2, 12:00 - 13:00

  KAUST

  Microelectromechanical Digital Circuit design Low Power computation Sensing

  Electromechanical switches were the core elements of the very first digital computers in early 20th century. While these switches were later replaced by the smaller, faster and more reliable "transistor" technology, they found a new life following the development of nanofabrication tools and Micro-electromechannical Systems (MEMS). In this seminar we will explore the most recent advances in the field of MEMS-based digital circuit and sensor design. We also examine the application of MEMS switches and resonators in building the most important blocks of a digital system, namely adders, multipliers, data converters, sequential and combinational complex logic, and discuss the future of this technology in the beyond-CMOS era.

Apr 25 - May 1, 2021

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  The Role of Volcanic Activity in Climate and Global Change

  Georgiy L. Stenchikov, Professor, Earth Science and Engineering

  Apr 29, 12:00 - 13:00

  KAUST

  Explosive volcanic eruptions are magnificent events that in many ways affect the Earth’s natural processes and climate. They cause sporadic perturbations of the planet’s energy balance, activating complex climate feedbacks and providing unique opportunities to better quantify those processes. We know that explosive eruptions cause cooling in the atmosphere for a few years, but we have just recently realized that they affect the major climate variability modes and volcanic signals can be seen in the subsurface ocean for decades. The volcanic forcing of the previous two centuries offsets the ocean heat uptake and diminishes global warming by about 30%. In the future, explosive volcanism could slightly delay the pace of global warming and has to be accounted for in long-term climate predictions. The recent interest in dynamic, microphysical, chemical and climate impacts of volcanic eruptions is also excited by the fact these impacts provide a natural analog for climate geoengineering schemes involving the deliberate development of an artificial aerosol layer in the lower stratosphere to counteract global warming. In this talk, I will discuss these recently discovered volcanic effects and specifically pay attention to how we can learn about the hidden Earth-system mechanisms activated by explosive volcanic eruptions.
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  Design and Analysis of Prevalence Surveys for Neglected Tropical Diseases

  Prof. Peter Diggle, Statistics in the faculty of Health and Medicine, Lancaster University

  Apr 27, 15:00 - 16:30

  KAUST

  Design and analysis of surveys

  In low-resource settings, disease registries do not exist, and prevalence mapping relies on data collected form surveys of disease prevalence taken in a sample of the communities at risk within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model, Yᵢ ~ Binomial(mᵢ; P(xᵢ)) log [P(x)/{(1- P(xᵢ)}] = d(x)β + S(x), where Yᵢ is the number of positives in a sample of mi individuals at location xᵢ, d(x) is a vector of spatially referenced explanatory variables available at any location x within the region of interest, and S(x) is a Gaussian process. In this talk, I will first review statistical methods and software associated with this standard model, then consider several methodological extensions and their applications to some Africa-wide control programmes for Neglected Tropical Diseases to demonstrate the very substantial gains in efficiency that can be obtained by comparison with currently used methods.
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  Dynamical Modeling, Analysis, Monitoring and Control of Wastewater Treatment Processes - Part 2B

  Prof. Denis Dochain, ICTEAM, Université Catholique de Louvain

  Apr 27, 14:00 - 15:30

  KAUST

  wastewater treatment adaptive linearizing control dynamical modeling

  There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.
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  Dynamical Modeling, Analysis, Monitoring and Control of Wastewater Treatment Processes - Part 2A

  Prof. Denis Dochain, ICTEAM, Université Catholique de Louvain

  Apr 27, 10:30 - 12:00

  KAUST

  wastewater treatment adaptive linearizing control dynamical modeling

  There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.
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  Energy-Efficiency and Security for EdgeAI: Challenges and Opportunities

  Muhammad Shafique , Professor, Division of Engineering, New York University Abu Dhabi (NYU-AD), United Arab Emirates

  Apr 26, 12:00 - 13:00

  KAUST

  Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power-constrained scenarios. Therefore, such systems need to support not only the high-performance capabilities under tight power/energy envelop, but also need to be intelligent/cognitive and robust. This has given rise to a new age of Machine Learning (and, in general Artificial Intelligence) at different levels of the computing stack, ranging from Edge and Fog to the Cloud. In particular, Deep Neural Networks (DNNs) have shown tremendous improvement over the past years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. However, these DNN require highly complex computations, incurring huge processing, memory, and energy costs. To some extent, Moore’s Law help by packing more transistors in the chip.
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  Applied Complexity: Ultra-flat AI materials for different applications

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Apr 25, 12:00 - 13:00

  KAUST

  We review our recent results on artificial-intelligent designed ultra-flat materials that embeds "physical" neural networks for different application in biomedical imaging, optics, displays, and structural color generation.

Apr 18 - Apr 24, 2021

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  Introduction to hybrid dynamical systems: Part 2B

  Prof. Mohamed Djemai, University Polytechnique Hauts-de-France

  Apr 22, 14:00 - 15:30

  KAUST

  Dynamical Systems Modelization Time Scale Approach

  This research is motivated not only because the control of some systems is implemented through the combination of continuous control laws with discrete switching logic but also because a wide range of physical and engineering systems exhibit hybrid behavior. Among the problems to be addressed, those of stabilization and observation are particularly important in order to always improve the efficiency of systems in terms of performance, lifetime and efficiency.
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  Distributed second order methods with fast rates and compressed communication

  Peter Richtarik, Professor, Computer Science

  Apr 22, 12:00 - 13:00

  KAUST

  We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inherits its fast local quadratic rate. However, unlike Newton's method, NEWTON-STAR enjoys the same per iteration communication cost as gradient descent. While this method is impractical as it relies on the use of certain unknown parameters characterizing the Hessian of the objective function at the optimum, it serves as the starting point which enables us to design practical variants thereof with strong theoretical guarantees. In particular, we design a stochastic sparsification strategy for learning the unknown parameters in an iterative fashion in a communication efficient manner. Applying this strategy to NEWTON-STAR leads to our next method, NEWTON-LEARN, for which we prove local linear and superlinear rates independent of the condition number. When applicable, this method can have dramatically superior convergence behavior when compared to state-of-the-art methods. Finally, we develop a globalization strategy using cubic regularization which leads to our next method, CUBIC-NEWTON-LEARN, for which we prove global sublinear and linear convergence rates, and a fast superlinear rate. Our results are supported with experimental results on real datasets, and show several orders of magnitude improvement on baseline and state-of-the-art methods in terms of communication complexity.
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  Introduction to hybrid dynamical systems: Part 2A

  Prof. Mohamed Djemai, University Polytechnique Hauts-de-France

  Apr 22, 10:30 - 12:00

  KAUST

  Dynamical Systems Modelization Time Scale Approach

  In recent years, new theoretical tools have been developed to describe complex systems more precisely, such as hybrid dynamical systems (HDS). Many works ranging from modelling to stabilisation, or control and observation have focused on the study of this class of systems. This research is motivated not only because the control of some systems is implemented through the combination of continuous control laws with discrete switching logic but also because a wide range of physical and engineering systems exhibit hybrid behavior. Among the problems to be addressed, those of stabilization and observation are particularly important in order to always improve the efficiency of systems in terms of performance, lifetime and efficiency.
• 

  Dynamical Modeling, Analysis, Monitoring and Control of Wastewater Treatment Processes - Part 1B

  Prof. Denis Dochain, ICTEAM, Université Catholique de Louvain

  Apr 20, 14:00 - 15:30

  KAUST

  wastewater treatment adaptive linearizing control dynamical modeling

  There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.
• 

  Dynamical Modeling, Analysis, Monitoring and Control of Wastewater Treatment Processes - Part 1A

  Prof. Denis Dochain, ICTEAM, Université Catholique de Louvain

  Apr 20, 10:30 - 12:00

  KAUST

  wastewater treatment adaptive linearizing control dynamical modeling

  The objective of this course is to give an introduction and cover recent aspects of dynamical modeling, monitoring and control of wastewater treatment processes. There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.
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  How do people explore virtual environments? Capturing and modeling visual behavior in VR

  Belen Masia, Associate Professor in the Computer Science Department at Universidad de Zaragoza

  Apr 19, 12:00 - 13:00

  KAUST

  Virtual Reality (VR) can dramatically change the way we create and consume content in areas of our everyday life, including entertainment, training, design, communication or advertising. Understanding how people explore immersive virtual environments is crucial for many applications in VR, such as designing content, developing new compression algorithms, or improving the interaction with virtual humans. In this talk, we will focus on how to capture and model visual behavior of users in virtual environments.
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  KL Divergence Regularized Learning Model for Multi-Agent Decision Making

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Apr 18, 12:00 - 13:00

  KAUST

  The large population game framework has been widely adopted in biology, economics, and engineering fields to model and analyze strategic interactions among decision-making agents. In this framework, a population of agents select strategies of interaction with one another and repeatedly revise their strategy choices using revisions defined by a decision-making model. While many of existing works in the literature focus on designing decision-making models that ensure convergence of the agents’ strategy revision to Nash equilibrium, a still open challenge is to establish the convergence when the agents’ strategy revision is subject to time delay. Such scenarios include multi-agent decision problems in which there is delay in propagation of traffic congestion in congestion games, communication between the electric power utility and demand response agents in demand response games, and information transmission between agents in network games. In this seminar, I’ll introduce our recent work on designing a new decision-making model called the Kullback-Leibler (KL) divergence regularized learning. We will discuss how the new model enables a large population of agents to learn and self-organize to an effective strategy profile in population games subject to time delay and implication of the new model in engineering applications.

Apr 11 - Apr 17, 2021

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  Introduction to hybrid dynamical systems: Part 1B

  Prof. Mohamed Djemai, University Polytechnique Hauts-de-France

  Apr 15, 14:00 - 15:30

  KAUST

  Dynamical Systems Modelization Time Scale Approach

  This research is motivated not only because the control of some systems is implemented through the combination of continuous control laws with discrete switching logic but also because a wide range of physical and engineering systems exhibit hybrid behavior. Among the problems to be addressed, those of stabilization and observation are particularly important in order to always improve the efficiency of systems in terms of performance, lifetime and efficiency.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2021

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Apr 15, 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. 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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  Introduction to hybrid dynamical systems: Part 1A

  Prof. Mohamed Djemai, University Polytechnique Hauts-de-France

  Apr 15, 10:30 - 12:00

  KAUST

  Dynamical Systems Modelization Time Scale Approach

  In recent years, new theoretical tools have been developed to describe complex systems more precisely, such as hybrid dynamical systems (HDS). Many works ranging from modelling to stabilisation, or control and observation have focused on the study of this class of systems.
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  Continuous-time optimal control on discrete spaces. Applications to inventory management in commerce and finance - Session 2

  Olivier Guéant, Professor, Applied Mathematics at Université Paris 1 Panthéon-Sorbonne, France

  Apr 13, 15:00 - 18:00

  KAUST

  This 6-hour course covers the theory of optimal control in the case of discrete spaces / graphs. In the first part, we present the dynamic programming principle and the resulting Bellman equations. Bellman equations, which turn out to be a system of backward ordinary differential equations (ODE), are then thoroughly studied: in addition to existence and uniqueness results obtained through classical ODE tools and comparison principles, the long-term behavior of optimal control problems is studied using comparison principles and semi-group tools. The second part of the course focuses on a special case of optimal control problems on graphs for which closed-form solutions can be derived. The link with inventory management problems will be presented in details (in particular the link with the resolution of the Avellaneda-Stoikov problem, a classical problem in finance).
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  Ingesting and Processing Data Efficiently for Machine Learning

  Ana Klimovic, Assistant Professor, Systems Group of the Computer Science Department, ETH Zurich.

  Apr 12, 12:00 - 13:00

  KAUST

  Machine learning applications have sparked the development of specialized software frameworks and hardware accelerators. Yet, in today’s machine learning ecosystem, one important part of the system stack has received far less attention and specialization for ML: how we store and preprocess training data. This talk will describe the key challenges for implementing high-performance ML input data processing pipelines.
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  Pointing and Trajectory Tracking Control for Optical Wireless Communication

  Ibrahima N’Doye, Research Scientist, Electrical and Computer Engineering (ECE), CEMSE, KAUST

  Apr 11, 12:00 - 13:00

  KAUST

  Pointing Trajectory tracking control Optical wireless communication

  In this talk, I will present our recent works on reducing the beam pointing error for improved free-space optical communication (FSO) link performance. Specifically, I will discuss a robust control strategy that reduces the beam alignment error under controlled weak turbulence conditions for FSO systems. Then, I will discuss localization and tracking control of a mobile target ship with an autonomous underwater vehicle (AUV) in underwater environment. The framework is designed using a hybrid acoustic-optical underwater communication to drive the AUV to the maximum achievable data rate angle. The acoustic link is used for non-line-of-sight localization, and the optical link is for line-of-sight transmission. I will conclude the talk by providing recent results on estimating the alignment angle through a novel estimation-based reference trajectory control algorithm for an LED-based optical communication model.

Apr 4 - Apr 10, 2021

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  A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis

  Xin Gao, Program Chair, Computer Science

  Apr 8, 12:00 - 13:00

  KAUST

  COVID-19

  COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this talk, I will introduce our recent work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) an embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.
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  Discriminant Analysis and Support Vector Regression in High Dimensions: Sharp Performance Analysis and Optimal Designs

  Houssem Sifaou, Ph.D., Electrical and Computer Engineering

  Apr 8, 11:00 - 13:00

  KAUST

  Machine learning is emerging as a powerful tool to data science and is being applied in almost all subjects. In many applications, the number of features is comparable or even larger than the number of samples, and both grow large. This setting is usually named the high-dimensional regime. In this regime, new challenges and questions arise when it comes to the application of machine learning. In this work, we conduct a high-dimensional performance analysis of some popular classification and regression techniques. In a first part, discriminant analysis classifiers are considered. A major challenge towards the use of these classifiers in practice is that they depend on the inverse of covariance matrices that need to be estimated from training data. Several estimators for the inverse of the covariance matrices can be used. The most common ones are estimators based on the regularization approach. The main advantage of such estimators is their resilience to the sampling noise, making them suitable to high-dimensional settings. In this thesis, we propose new estimators that are shown to yield better performance.
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  Continuous-time optimal control on discrete spaces. Applications to inventory management in commerce and finance - Session 1

  Olivier Guéant, Professor, Applied Mathematics at Université Paris 1 Panthéon-Sorbonne, France

  Apr 6, 15:00 - 18:00

  KAUST

  This 6-hour course covers the theory of optimal control in the case of discrete spaces / graphs. In the first part, we present the dynamic programming principle and the resulting Bellman equations. Bellman equations, which turn out to be a system of backward ordinary differential equations (ODE), are then thoroughly studied: in addition to existence and uniqueness results obtained through classical ODE tools and comparison principles, the long-term behavior of optimal control problems is studied using comparison principles and semi-group tools. The second part of the course focuses on a special case of optimal control problems on graphs for which closed-form solutions can be derived. The link with inventory management problems will be presented in details (in particular the link with the resolution of the Avellaneda-Stoikov problem, a classical problem in finance).
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  Challenges of Differentially Private Empirical Risk Minimization

  Di Wang, Assistant Professor, Computer Science

  Apr 5, 12:00 - 13:00

  KAUST

  Recent research showed that most of the existing machine learning algorithms are vulnerable to various privacy attacks. An effective way for defending these attacks is to enforce differential privacy during the learning process. As a rigorous scheme for privacy preserving, Differential Privacy (DP) has now become a standard for private data analysis. Despite its rapid development in theory, DP's adoption to the machine learning community remains slow due to various challenges from the data, the privacy models and the learning tasks. In this talk, I will give a brief introduction on DP and use the Empirical Risk Minimization (ERM) problem as an example and show how to overcome these challenges in DP model. Particularly, I will first talk about how to overcome the high dimensionality challenge from the data for Sparse Linear Regression in the local DP (LDP) model. Then, I will discuss the challenge from the non-interactive LDP model and show a series of results to reduce the exponential sample complexity of ERM. Next, I will present techniques on achieving DP for ERM with non-convex loss functions. Finally, I will discuss some future research along these directions.