Events

Sep 5 - Sep 12, 2021

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  Computational Imaging and Its Applications in Fluids

  Jinhui Xiong, Ph.D. Student, Computer Science

  Sep 6, 16:00 - 17:00

  KAUST

  Computational imaging differs from traditional imaging systems by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D-3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color.
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  A Toolkit for Multi-Faceted Geospatial Data Protection in Transactional and Analytical Processing using Searchable Encryption and Differential Privacy

  Gabriel Ghinita, Associate Professor, University of Massachusetts, Boston

  Sep 6, 12:00 - 13:00

  B9 R2322 H1

  The mobile revolution of the past decade led to the ubiquitous presence of location data in all application domains, ranging from public safety and healthcare to urban planning, transportation and commercial applications. Numerous services rely on location data to provide customized service to their users. At the same time, there are serious concerns with respect to protecting individual privacy, as location traces can disclose sensitive details to an untrusted service.

Aug 29 - Sep 12, 2021

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  Microwave Water Cut Sensors for Oil Industry: From Design to Deployment in the Field

  Muhammad A. Karimi, Chief Technology Officer, Saher Flow Solutions

  Sep 5, 12:00 - 13:00

  KAUST

  This talk will be focused on developing low-cost sensors, which can increase oil production efficiency through real-time monitoring of oil wells. The mechanism behind the development of low-cost & printed microwave sensors will be introduced followed by a number of applications where these sensors can be utilized. A specific application to measure the composition of production fluid will be described in detail. Unique benefits of Microwave DMOR technology, utilized in the sensors, will be explained in comparison with existing technologies.

Aug 29 - Sep 5, 2021

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  High-resolution Modeling and Estimation of Extreme Red Sea Surface Temperature Hotspots

  Raphaël Huser, Associate Professor, Statistics

  Sep 2, 12:00 - 13:00

  KAUST

  Modeling, estimation and prediction of spatial extremes is key for risk assessment in a wide range of geo-environmental, geo-physical, and climate science applications. In this work, we propose a flexible approach for modeling and estimating extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the whole Red Sea, a vital region of high biodiversity.
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  Sequential Monte Carlo Algorithms for Agent-Based Models of Disease Transmission

  Jeremy Heng, Applied Mathematics and Computational Sciences (AMCS), ESSEC Business School, Singapore

  Sep 1, 13:00 - 14:00

  KAUST

  We consider statistical inference for a class of agent-based SIS and SIR models. In these models, agents infect one another according to random contacts made over a social network, with an infection rate that depends on individual attributes. Infected agents might recover according to another random mechanism that also depends on individual attributes, and observations might involve occasional noisy measurements of the number of infected agents. Likelihood-based inference for such models presents various computational challenges. In this talk, I will present various sequential Monte Carlo algorithms to address these challenges.
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  Large-Scale Visualization of Scientific Data: Volume Data and Flow Fields - 2021-08-30

  Markus Hadwiger, Professor, Computer Science

  Aug 30, 12:00 - 13:00

  B9 R2322 H1

  This talk will give an overview of the research of the High-Performance Visualization research group (vccvisualization.org) at the KAUST Visual Computing Center (VCC). Interactive visualization is crucial to exploring, analyzing, and understanding large-scale scientific data, such as the data acquired in medicine or neurobiology using computed tomography or electron microscopy, and data resulting from large-scale simulations such as fluid flow in the Earth’s atmosphere and oceans. The amount of data in data-driven science is increasing rapidly toward the petascale and further.

Aug 22 - Sep 5, 2021

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  Dark Vision: A Sensory Eye into Subsurface Wellbore Casings

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Aug 29, 12:00 - 13:00

  KAUST

  Dark Vision Sensory Eye Subsurface Wellbore

  After a quick overview of the ECE Graduate Seminar logistics, I will share a quick introduction to the wellbore construction process. This will help build the case for maintaining wellbore integrity in order to protect assets, people, the environment and production. The synergistic integration of electromagnetics, electronics and machine learning to create a novel mechatronic solution to address wellbore integrity needs is then discussed. The solution utilizes a full maxwell equations solver deployed on KAUST’s super computing platforms to enable next generation physics informed wellbore integrity solutions based on non-contact EM field propagation circuits. While downhole camera technologies are used today, they require illumination and an optically clear environment. Our electromagnetic ‘vision’ system overcomes these limitations and provides additional capability to ‘see through’ nested wellbore tubulars.

Aug 15 - Aug 22, 2021

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  Machine Learning in Healthcare: When Low Sample Size is not a Limitation

  Dr. Ricardo Henao, Biostatistics and Bioinformatics, Duke University

  Aug 17, 14:30 - 15:30

  KAUST

  In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.
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  Applications of Magnetic Transition Metal Dichalcogenide Monolayers to the Field of Spinorbitronics

  Idris H. Smaili, Ph.D. Student, Electrical and Computer Engineering

  Aug 16, 11:00 - 13:00

  KAUST

  flexible nanodevices flexible solar cells nanomaterials thin films density functional theory

  Magnetic random access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Consequently, there is now an urgent need for new low-dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writing, and storing data.

Aug 8 - Aug 22, 2021

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  AMCS Workshop for Higher Education Instructors

  Aug 15, 10:00 - Aug 26, 16:00

  KAUST Campus

  linear algebra Programming language

  Workshop's Agenda View Workshop's Agenda. Topics for the event: Core sessions on Linear Algebra Core sessions on applications on emerging teaching methodologies Programming language (practical applications) Keynote lectures on emerging teaching methodologies and current research applications Core lab and research center visits and presentations Networking sessions and social activities with KAUST faculty, researchers, alumni, and students This workshop is for TAs and lecturers from in-Kingdom Universities. It is not open to KAUST students and the community. Applications are closed now!

Aug 8 - Aug 15, 2021

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  Stylistic and spatial disentanglement in GANs

  Yazeed Alharbi, Ph.D., Computer Science

  Aug 12, 14:00 - 16:00

  KAUST

  Computer Vision machine learning generative adversarial network Deep learning

  This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Jul 25 - Aug 1, 2021

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  Stochastic Optimization in Target Positioning and Location-based Applications

  Hui Chen, Ph.D. Student (former), Electrical and Computer Engineering

  Jul 27, 17:00 - 19:00

  KAUST

  stochastic algorithm optimization Hand Gestures Recognition

  This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.

Jul 4 - Jul 11, 2021

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  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2C

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

  Jul 7, 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
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  State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2B

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

  Jul 6, 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 2A

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

  Jul 5, 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

Jun 27 - Jul 4, 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

Jun 20 - Jul 4, 2021

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  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 20, 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.
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  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.
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  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 13, 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.
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  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.
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  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 6, 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.
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  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
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  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.
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  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.
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  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.
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  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 30, 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 16, 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 9, 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)
• 

  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.

Apr 25 - May 9, 2021

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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 2, 2021

• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.

Apr 18 - May 2, 2021

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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 25, 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.
• 

  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.