Events

Mar 28 - Apr 3, 2021

• 

  Human Robot Interaction with Natural Language Logic: a Step Closer

  Emmanuel Roche, PhD, CEO of Clover.AI

  Mar 31, 15:45 - 16:30

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Using drones, fluid lensing, and network analysis to get a more comprehensive view of coral reef mapping in the Red Sea

  Colleen Campbell, PhD Student, Red Sea Research Center, KAUST

  Mar 31, 14:15 - 15:00

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Trajectory planning in uncertain transient currents: a stochastic optimization approach

  Ricardo M. Lima, Research Scientist, CEMSE, KAUST

  Mar 31, 13:30 - 14:15

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Enhancing visibility for reliable underwater visual SLAM

  Ayoung Kim, Associate Professor, Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)

  Mar 31, 12:45 - 13:30

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Challenges in Marine Autonomy

  Stefan B. Williams, Head of School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney

  Mar 31, 12:00 - 12:45

  KAUST

  Featured Sessions

  Autonomous Underwater Vehicles Simultaneous Localisation and Mapping machine learning

  For more information please visit the website
• 

  Numerical Methods for Mean Field Games- Session 2

  Mathieu Laurière, Postdoc, Operations Research and Financial Engineering, Princeton University, USA

  Mar 30, 14:30 - 17:30

  KAUST

  Mean field games and mean field control problems are frameworks to study Nash equilibria or social optima in games with a continuum of agents. These problems can be used to approximate competitive or cooperative situations with a large finite number of agents, and have found a broad range of applications, from economics to crowd motion, energy production and risk management. The solutions are typically characterized by a forward-backward system of partial differential equations (PDE) or stochastic differential equations (SDE).
• 

  Multi-Scale GPU Resource Management for Deep Learning

  Mosharaf Chowdhury, Morris Wellman, Assistant Professor of CSE at the University of Michigan, Ann Arbor

  Mar 29, 18:30 - 19:30

  KAUST

  GPUs have emerged as a popular choice for deep learning. To deal with ever-growing datasets, it is also common to use multiple GPUs in parallel for distributed deep learning. Although achieving cost-effectiveness in these clusters relies on efficient sharing, modern GPU hardware, deep learning frameworks, and cluster managers are not designed for efficient, fine-grained sharing of GPU resources. In this talk, I will present our recent works on efficient GPU resource management, both within a single GPU and across many GPUs in a cluster for hyperparameter tuning, training, and inference. The common thread across all our works is leveraging the interplay between short-term predictability and long-term unpredictability of deep learning workloads
• 

  Towards Robust and Accurate Navigation: Theory, Applications, and Future Directions

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Mar 28, 12:00 - 13:00

  KAUST

  Towards Robust Navigation theory Applications Future Directions

  Navigation is an essential requirement for many applications (commercial, retail, military, scientific, ...etc) and in a variety of environments (in-doors, outdoors, space, underwater, and even underground). In this talk, I will overview some of my group's work in localization and navigation focusing on indoor and satellite positioning. The talk will demonstrate how the structure or constraints of the problem can help achieve very accurate localization (e.g. millimeter level indoors) that is robust to Doppler, multipath, and shadowing. The talk will also touch upon various related applications that the group is pursuing in smart health and smart cities. The talk will end with future directions for localization in extreme environments and in the TeraHertz spectrum where localization, environment sensing, and communication converge.

Mar 21 - Mar 27, 2021

• 

  The Convex Gaussian Min-Max Theorem: A Powerful Tool for the Analysis of Regularized Convex Optimization Problems

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Mar 25, 12:00 - 13:00

  KAUST

  In modern large-scale inference problems, the dimension of the signal to be estimated is comparable or even larger than the number of available observations. Yet the signal of interest lies in some low-dimensional structure, due to sparsity, low-rankness, finite alphabet, ... etc. Non-smooth regularized convex optimization are powerful tools for the recovery of such structured signals from noisy linear measurements. Research has shifted recently to the performance analysis of these optimization tools and optimal turning of their hyper-parameters in high dimensional settings. One powerful performance analysis framework is the Convex Gaussian Min-max Theorem (CGMT). The CGMT is based on Gaussian process methods and is a strong and tight version of the classical Gordon comparison inequality. In this talk, we review the CGMT and illustrate its application to the error analysis of some convex regularized optimization problems.
• 

  International Seminar on Mathematical Models and Numerical Methods for Flow and Transport in Porous Media

  Speakers from KAUST, CEMSE, PSE, G-CSC, IBRAE and INM RAS

  Mar 25, 10:30 - Mar 26, 17:00

  KAUST

  numerical methods Mathematical modeling flow problems porous media

  This workshop is devoted to numerical simulation of groundwater flow and subsurface contamination transport, as well as related problems. The main topics are mathematical modeling of the processes in porous media and the numerical methods for discretization, solution of the discretized systems and numerical treatment of inverse problems. In particular, fractured porous media and partially saturated aquifers will be concerned.
• 

  Numerical Methods for Mean Field Games - Session 1

  Mathieu Laurière, Postdoc, Operations Research and Financial Engineering, Princeton University, USA

  Mar 23, 14:30 - 17:30

  KAUST

  Mean field games and mean field control problems are frameworks to study Nash equilibria or social optima in games with a continuum of agents. These problems can be used to approximate competitive or cooperative situations with a large finite number of agents, and have found a broad range of applications, from economics to crowd motion, energy production and risk management. The solutions are typically characterized by a forward-backward system of partial differential equations (PDE) or stochastic differential equations (SDE)
• 

  Automated Reasoning in Systems Engineering

  Laura Kovacs, Professor in Computer Science at the TU Wien

  Mar 22, 12:00 - 13:00

  KAUST

  In this talk I will present recent advancement in automated reasoning, in particular computer-supported theorem proving, for generating and proving software properties that prevent programmers from introducing errors while making changes in this software. When testing programs manipulating the computer memory, our initial results show our work is able to prove that over 80% of test cases are guaranteed to have the expected behavior.
• 

  Minimal-time Mean Field Games

  Samer Dweik, Postdoctoral Fellow, University of British Columbia

  Mar 22, 11:00 - 12:00

  KAUST

  In this talk, we study a mean field game inspired by crowd motion in which agents evolve in a domain and want to reach its boundary minimizing their travel time. Interactions between agents occur through their dynamic, which depends on the distribution of all agents. First, we provide a Lagrangian formulation for our mean field game and prove existence of equilibria, which are shown to satisfy a MFG system. The main result, which relies on the semi-concavity of the value function of this optimal control problem, states that an L^p initial distribution of agents gives rise to an L^p distribution of agents at each time t>0.
• 

  Advancing Visual Intelligence via Neural System Design

  Hengshuang Zhao, Postdoctoral Fellow, Oxford University

  Mar 21, 12:00 - 13:00

  KAUST

  Visual Intelligence Neural System design

  Building intelligent visual systems is essential for the next generation of artificial intelligence systems. It is a fundamental tool for many disciplines and beneficial to various potential applications such as autonomous driving, robotics, surveillance, augmented reality, to name a few. An accurate and efficient intelligent visual system has a deep understanding of the scene, objects, and humans. It can automatically understand the surrounding scenes. In general, 2D images and 3D point clouds are the two most common data representations in our daily life. Designing powerful image understanding and point cloud processing systems are two pillars of visual intelligence, enabling the artificial intelligence systems to understand and interact with the current status of the environment automatically. In this talk, I will first present our efforts in designing modern neural systems for 2D image understanding, including high-accuracy and high-efficiency semantic parsing structures, and unified panoptic parsing architecture. Then, we go one step further to design neural systems for processing complex 3D scenes, including semantic-level and instance-level understanding. Further, we show our latest works for unified 2D-3D reasoning frameworks, which are fully based on self-attention mechanisms. In the end, the challenges, up-to-date progress, and promising future directions for building advanced intelligent visual systems will be discussed.

Mar 14 - Mar 20, 2021

• 

  Multicomponent flows: overview of the models and results on the existence of global in time solutions - Part 2

  Ewelina Zatorska, Senior Lecturer, Applied and Numerical Analysis, Imperial College London, UK

  Mar 18, 14:00 - 16:00

  KAUST

  In this lecture I will present broader spectrum of complex, multicomponent flows. For example, the models of compressible mixtures describe multicomponent fluids that are mixed on the molecular level. They are different from the models of the multi-phase flows from the first lecture, because there is no division of volume occupied by different species. The existence of global in time weak solutions, and global in time strong solutions for such systems will be explained, and some open problems related to singular limits and weak-strong uniqueness of solutions will be mentioned. At the end of the lecture I will also present another model of two-phase flow describing the motion of compressible and incompressible medium with an interphase given by a condition on the density. I will explain how to prove the existence of solutions and give some applications in modelling of crowd evacuation.
• 

  On Discovering Nature’s Equations from Observations

  Jesper Tegner, Professor, BESE Division, KAUST

  Mar 18, 12:00 - 13:00

  KAUST

  In essence, science is about discovering regularities in Nature. It turns out that such regularities (laws) are written in the language of mathematics. In many cases, such laws are formulated and refined from fundamental “first principles.” Yet, in phenomenological areas such as biology, we have an abundance of data but lack “first principles.” Machine learning and deep learning, in particular, are remarkably successful in classification and prediction tasks. However, such systems, when trained on data, do not, as a rule, provide compact mathematical laws or fundamental first principles. Here we ask how we can identify interpretable compact mathematical laws from complex data-sets when we don’t have access to first principles. I will give an overview of this problem and provide some vignettes of our ongoing work in attacking this problem.
• 

  Fully nonlinear degenerate elliptic equations: qualitative properties of viscosity solutions - Session 3

  Martino Bardi, Professor, Mathematical Sciences, University of Padova, Italy

  Mar 17, 15:00 - 17:00

  KAUST

  I will start recalling the definitions and basic properties of viscosity solutions to fully nonlinear degenerate elliptic equations, in particular the comparison principles. The main goal of the course is discussing two properties of subsolutions: the Strong Maximum Principle (SMP), i.e., if a subsolution in an open connected set attains an interior maximum then it is constant, and the Liouville property, i.e., if a subsolution in the whole space is bounded form above then it is constant. They are standard results for classical solutions of linear elliptic PDEs, and many extensions are known, especially for divergence form equations. My goal is explaining how the viscosity methods allow to turn around the difficulties of non-smooth solutions, fully nonlinear equations, and their possible degeneracies.
• 

  Mathematical Modeling and Analysis of Emergent Phenomena

  Jan Haskovec, Research Scientist, AMCS, KAUST

  Mar 15, 12:00 - 13:00

  KAUST

  Emergence of nontrivial patterns via collective actions of many individual entities is an ever-present phenomenon in physics, biology and social sciences. It has numerous applications in engineering, for instance, in swarm robotics. I shall demonstrate how tools from mathematical modeling and analysis help us gain understanding of fundamental principles and mechanisms of emergence. I will present my recent results in consensus formation and flocking models, focusing on the effects of noise and delay on their dynamics. Moreover, I will introduce continuum modeling framework for biological network formation, where emergence takes place through the interaction of structure and medium. The models are formulated in terms of ordinary, stochastic and partial differential equations. I shall explain how mathematical analysis of the respective models contributes to the understanding of how individual rules generate and influence the patterns observed on the global scale. A particular example from biology is development of leaf venation as a result of auxin-PIN interaction in the plant tissue. Here our model supported the hypothesis that a-priori polarization of auxin transport does not play a decisive role in leaf venation.
• 

  mm-Sized Wireless Implantable Medical Devices Toward Clinical Diagnostics and Therapeutics

  Pyungwoo Yeon, Postdoctoral Fellow, Electrical Engineering, Stanford University

  Mar 14, 16:00 - 17:00

  KAUST

  Implantable and wearable technologies are rapidly emerging and showing great promise in various medical therapeutics and diagnostics. These advances from health monitoring to therapeutic applications reorganize many aspects of daily life. Since the invention of the first implantable cardiac pacemaker for patients with arrhythmia in 1958, many implantable medical devices (IMDs) such as implantable cardioverter defibrillators (ICDs) and implantable deep brain stimulators (iDBSs) have been developed and have treated millions of patients. These IMDs eventually need wireless powering and communication for 1) eliminating transcutaneous power/data interconnects that elevate the risk of infection and 2) removing bulky batteries to avoid the risk of repeated surgical intervention. They also need small form factors and soft materials to mitigate tissue fibrosis due to foreign body responses. In this talk, I will introduce a new wireless neural interfacing tool within a cubic millimeter that can potentially record large scale neuronal ensembles over large brain area. The discussion will be focused on its building pieces across a wide range of science and engineering disciplines toward translating those into a complete clinically viable IMD. Also, I will present my recent research on RF-to-ultrasound power relay for powering deep mm-scale implants across air/tissue or tissue/skull media, and an all-soft and wireless pressure sensor tags that aim to address the issues of surgical complexity, tethering effect, and foreign body response. Finally, I will conclude the talk by presenting my future research plans toward clinically viable wireless medical diagnostics and therapeutics.

Mar 7 - Mar 13, 2021

• 

  Programming curvature into flat sheets

  Mar 11, 12:00 - 13:00

  KAUST

  Small-scale cut and fold patterns imposed on sheet material enable its morphing into three-dimensional shapes. This manufacturing paradigm has received much attention in recent years and poses challenges in both fabrication and computation. It is intimately connected with the interpretation of patterned sheets as mechanical metamaterials, typically of negative Poisson ratio. We discuss a fundamental geometric question, namely the targeted programming of a shape morph from a flat sheet to a curved surface, or even between any two shapes. The solution draws on differential geometry, discrete differential geometry, geometry processing and geometric optimization.
• 

  Fully nonlinear degenerate elliptic equations: qualitative properties of viscosity solutions - Session 2

  Martino Bardi, Professor, Mathematical Sciences, University of Padova, Italy

  Mar 10, 15:00 - 17:00

  KAUST

  I will start recalling the definitions and basic properties of viscosity solutions to fully nonlinear degenerate elliptic equations, in particular the comparison principles. The main goal of the course is discussing two properties of subsolutions: the Strong Maximum Principle (SMP), i.e., if a subsolution in an open connected set attains an interior maximum then it is constant, and the Liouville property, i.e., if a subsolution in the whole space is bounded form above then it is constant. They are standard results for classical solutions of linear elliptic PDEs, and many extensions are known, especially for divergence form equations. My goal is explaining how the viscosity methods allow to turn around the difficulties of non-smooth solutions, fully nonlinear equations, and their possible degeneracies.
• 

  Generation of Orbital Angular Momentum (OAM) Modes with Spiral Phase Plate Integrated Laser Source

  Edgars Stegenburgs, Ph.D. Student, Electrical and Computer Engineering

  Mar 10, 12:00 - 14:00

  KAUST

  The objective of this work is to develop a near-infrared laser device capable of emitting orbital angular momentum (OAM) light. The prototyped device must be suitable for compact, energy-saving optical communication applications. Integrated OAM lasers would revolutionize high capacity data transmission over any telecommunication network environment as OAM light can be guided and transmitted through kilometers of optical fibers as well as propagated in free space and underwater.
• 

  Analysis of the compressible two-phase flow

  Dr. Ewelina Zatorska, Senior Lecturer in the Applied and Numerical Analysis, Imperial College London

  Mar 9, 15:00 - 16:00

  KAUST

  In this talk, I will present the recent developments in the topic of the existence of solutions to the two-fluid systems. The compensated compactness technique of P.-L. Lions and E. Feireisl for single-component fluids has certain limitations, distinctly in the context of multi-component flow models. A particular example of such a model is the two-fluids Stokes system with a single velocity field and two densities, and with an algebraic pressure law closure. The first result that I will present is the existence of weak solutions for such a system, using the compactness criterion introduced recently by D. Bresch and P.-E. Jabin. I will also outline an innovative construction of solutions relying on the G. Crippa and C. DeLellis stability estimates for the transport equation. In the last part of my talk, I will relate to a couple of more recent results: the existence of solutions to the one-dimensional system, non-uniqueness of solutions to the inviscid system, and I will comment on issues around weak-strong uniqueness.
• 

  Fully nonlinear degenerate elliptic equations: qualitative properties of viscosity solutions - Session 1

  Martino Bardi, Professor, Mathematical Sciences, University of Padova, Italy

  Mar 8, 15:00 - 17:00

  KAUST

  I will start recalling the definitions and basic properties of viscosity solutions to fully nonlinear degenerate elliptic equations, in particular the comparison principles. The main goal of the course is discussing two properties of subsolutions: the Strong Maximum Principle (SMP), i.e., if a subsolution in an open connected set attains an interior maximum then it is constant, and the Liouville property, i.e., if a subsolution in the whole space is bounded form above then it is constant. They are standard results for classical solutions of linear elliptic PDEs, and many extensions are known, especially for divergence form equations. My goal is explaining how the viscosity methods allow to turn around the difficulties of non-smooth solutions, fully nonlinear equations, and their possible degeneracies.
• 

  ArtEmis: Affective Language for Visual Art

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Mar 8, 12:00 - 13:00

  KAUST

  We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, we focus on the affective experience triggered by visual artworks and ask the annotators to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice.
• 

  Materials and Devices for Printed Flexible Electronics

  Derya Baran, Assistant Professor, Material Science and Engineering, PSE, KAUST

  Mar 7, 12:00 - 13:00

  KAUST

  flexible electronics Flexible electronic devices

  The need for big data that the internet of things (IoT) has created in recent years has turned the focus on integrating the human body in the quest to understand it better, and in turn use such information for detection and prevention of harmful conditions. Applications in which continuous and uninterrupted operation is required, or where the use of external power sources may be challenging demands the use of self-powered autonomous systems. Organic photovoltaic devices are flexible, lightweight, and soft, capable of interacting with the human body and its mechanical demands. Their processability from solutions permits their adaptation to versatile fabrication techniques such as spin coating, roll-to-roll coating and inkjet printing, with benefits including low material usage and freedom of design. In this talk, I will present how organic photovoltaics can be utilized in printed electronics as energy harvesting devices and go through the historical progress of organic/hybrid photovoltaics as well as the main activities that are ongoing in my research lab ‘Omegalab’.

Feb 28 - Mar 6, 2021

• 

  A proof of the C^p'-regularity conjecture in the plane

  Prof. José Miguel Urbano, Mathematics at the University of Coimbra (Portugal)

  Mar 4, 12:00 - 13:00

  KAUST

  Abstract We establish a new oscillation estimate for solutions of nonlinear partial differential equations of degenerate elliptic type, which yields a precise control on the growth rate of solutions near their set of critical points. We then apply this new tool to prove the planar counterpart of the longstanding conjecture that solutions of the degenerate p-Poisson equation with a bounded source are locally of class C^p'=C^{1,1/p-1}, an optimal regularity result. This is joint work with Eduardo Teixeira (University of Central Florida, USA) and Damião Araújo (Universidade Federal da Paraíba
• 

  Title: Introduction to free boundary problems - Session 2

  Aram Karakhanyan, Professor, School of Mathematics, University of Edinburgh, UK

  Mar 2, 15:00 - 18:00

  KAUST

  In this course I will discuss the basics of the classical theory of free boundary problems. We will focus on two problems; the Alt-Caffarelli and obstacle problem. In the first part of the course we will discuss the regularity of the solutions, and in the remainder the full and partial regularity of the free boundary
• 

  Novel computational models in biological imaging

  Xin Gao, Program Chair, Computer Science

  Mar 1, 12:00 - 13:00

  KAUST

  In this talk, I will introduce our recent efforts on developing novel computational models in the field of biological imaging. I will start with the examples in electron tomography, for which I will introduce a robust and efficient scheme for fiducial marker tracking, and then describe a novel constrained reconstruction model towards higher resolution sub-tomogram averaging. I will then show our work on developing deep learning methods for super-resolution fluorescence microscopy.
• 

  SIAM CSE21 (VIRTUAL) - Minisymposium on HPC Response to the COVID19 Pandemic

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

  Mar 1, 09:00 - 18:00

  KAUST

  HPC ML COVID-19 artificial intelligence

  Abstract This minisymposium brings together experts in numerical simulation that have developed HPC software tools toward a better understanding coronavirus and the global COVID19 pandemic. Their recent results highlight the importance of computational science as a guiding tool that helps in uncovering the structure and mechanics of the viral spread as well as drastically reduces the number of candidate treatments that need laborious laboratory testing. From transformative medical advances to drug discovery driven by massively parallel supercomputing, this minisymposium will span a range of
• 

  On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

  Mar 1, 09:00 - 16:00

  KAUST

  applied statistics big data

  Abstract Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to
• 

  The fourth wave: ultrawide bandgap compound semiconductors for photonics and electronics

  Xiaohang Li, Assistant Professor, Electrical and Computer Engineering

  Feb 28, 12:00 - 13:00

  KAUST

  The fourth wave semiconductors photonics electronics

  Wide bandgap (WBG) compound semiconductors including GaN have shown enormous success in solid-state lighting, display, and electrification in recent decades due to superior properties such as direct bandgap, high electron mobility, and large breakdown field. They have been changing the world by elevating living standards and addressing grand challenges such as global warming. The pioneering researchers have been recognized by numerous accolades including the Nobel Prize and most recently, the Queen Elizabeth Prize. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) compound semiconductors with bandgap larger than 3.4 eV have attracted increasing attentions: they have been regarded as the 4th wave/generation 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, respectively, both crucial for human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. However, extensive multi-disciplinary studies of growth, materials, physics, and devices are essential to unearth the potentials due to the infancy. This seminar would cover the latest research on those aspects. It includes growth of state-of-the-art materials, discovery of unique material properties, and development of a widely adopted device physics framework for photonics and electronics especially short and long wavelength photonic devices.

Feb 21 - Feb 27, 2021

• 

  On Adversarial Network Attacks, Robustness, and Certification

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Feb 25, 12:00 - 13:00

  KAUST

  Abstract The outstanding performance of deep neural networks (DNNs), for visual recognition tasks in particular, has been demonstrated on many large-scale benchmarks. This performance has immensely strengthened the line of research that aims to understand and analyze the driving reasons behind the effectiveness of these networks. One important aspect of this analysis has gained much attention, namely the sensitivity of a DNN to perturbations. This has spawned a thrust in the research community, which focuses on developing adversarial attacks that fool a DNN, training strategies that make DNNs
• 

  Large-Scale Visualization of Scientific Data

  Markus Hadwiger, Professor, Computer Science

  Feb 23, 15:00 - 16:30

  KAUST

  data visualisation

  "A picture is worth a thousand words", and by going beyond static images, interactive visualization has become crucial to exploring, analyzing, and understanding large-scale scientific data. This is true for many areas of science and engineering, such as high-resolution imaging in neuroscience or materials science, as well as in large-scale fluid simulations of the Earth’s atmosphere and oceans, or of trillion-cell oil reservoirs. However, the fact that the amount of data in data-driven sciences is increasing rapidly toward the petascale, and further, presents a tremendous challenge to interactive visualization and analysis. Nowadays, an important enabler of interactivity is often the parallel processing power of GPUs, which, however, requires well-designed customized data structures and algorithms. Furthermore, scientific data sets do not only get larger, they also get more and more complex, and thus have become very hard to interpret and analyze. In this talk, I will give an overview of the research of my group in large-scale scientific visualization, from data structures and algorithms that enable petascale visualization on GPUs, to novel visual abstractions for interactive analysis of highly complex structures in neuroscience, to novel mathematical techniques that leverage differential geometric methods for the detection and visualization of features in large, complex fluid dynamics data on curved surfaces such as the Earth.
• 

  Title: Introduction to free boundary problems - Session 1

  Aram Karakhanyan, Professor, School of Mathematics, University of Edinburgh, UK

  Feb 23, 15:00 - 18:00

  KAUST

  In this course I will discuss the basics of the classical theory of free boundary problems. We will focus on two problems; the Alt-Caffarelli and obstacle problem. In the first part of the course we will discuss the regularity of the solutions, and in the remainder the full and partial regularity of the free boundary.
• 

  Look Here! Guiding Attention in Complex Visualizations

  Manuela Waldner, Assistant Professor at the Research Unit of Computer Graphics of the Institute of Visual Computing and Human-Centered Technology at TU Wien, Austria

  Feb 22, 12:00 - 13:00

  KAUST

  Drawing the user's attention to important items in an image, a complex visualization, or a cluttered graphical user interface is a non-trivial challenge. In the context of visualization, our goal is to effectively attract the user's attention to relevant items in large and complex scenes, while keeping noticeable modifications of the image to a minimum. In this talk, I will give an overview of common highlighting methods and present results from my research on attention guidance in complex, dynamic visualizations.
• 

  High-Dimensional Analysis of Regularized Convex Optimization Problems with Application to Massive MIMO Wireless Communication Systems

  Ayed M. Alrashdi, Ph.D. Student (former), Electrical and Computer Engineering

  Feb 21, 17:00 - 18:00

  KAUST

  In this thesis, we focus on precisely analyzing the high dimensional error performance of such regularized convex optimization problems under the presence of different impairments (such as uncertainties and/or correlations) in the measurement matrix, which has independent Gaussian entries. The precise nature of our analysis allows performance comparison between different types of these estimators and enables us to optimally tune the involved hyperparameters. In particular, we study the performance of some of the most popular cases in linear inverse problems, such as the Least Squares (LS), Regularized Least Squares (RLS), LASSO, Elastic Net, and their box-constrained variants.
• 

  Smart Meter Data-Driven Load Forecasting and Price Design in the Retail Market

  Dr. Yi Wang, Postdoctoral Researcher, Power Systems Laboratory, ETH Zurich

  Feb 21, 12:00 - 13:00

  KAUST

  Smart Meter Data-Driven Load forecasting Price design Retail Market

  The widespread popularity of smart meters enables an immense amount of fine-grained smart meter data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. Electricity retailer is one of the main participants in the retail market. This presentation will discuss how an electricity retailer can increase the competitiveness in the retail market by making full use of the fine-grained smart meter data. Since load forecasting is fundamental for various businesses of the retailer, the first part of this presentation will study how to enhance the performance of aggregated load forecasting using the fine-grained smart meter data. Designing customizing prices is an effective way to promote consumer interactions and increase customer stickiness for retailers. The second part of this presentation will introduce a novel data-driven approach for incentive-compatible customizing time-of-use (ToU) price design so that the benefits of both the retailer and consumers can be gained.

Feb 14 - Feb 20, 2021

• 

  Forward-prediction and Backward-design by Machine Learning

  Prof. Xiangliang Zhang

  Feb 18, 12:00 - 13:00

  KAUST

  Machine learning has been widely applied to diverse problems of forwarding prediction and backward design. The former problem of forwarding prediction is to predict the reaction of a system given the input x, i.e., y=f(x). This talk will introduce several groups of algorithms for learning the prediction function f. The backward design is an inverse problem, predicting the input x according to the system reaction y, i.e., x=g(y). This is an important problem for the design of chemical material and optical devices. This talk will introduce several successful application examples of machine learning algorithms on the backward design problems.
• 

  Nanovisualization: Depicting the Tiny World Around Us

  Ivan Viola, Associate Professor, Computer Science

  Feb 17, 11:30 - 13:00

  KAUST

  nanovisualization

  Life at micron-scale is inaccessible to the naked eye. To aid the comprehension of nano- and micro-scale structural complexity, we utilize 3D visualization. Thanks to efficient GPU-accelerated algorithms, we witness a dramatic boost in the sheer size of structures that can be visually explored. As of today an atomistic model of the entire bacterial cell can be displayed interactively. On top of that, advanced 3D visualizations efficiently convey the multi-scale hierarchical architecture and cope with the high degree of structural occlusion which comes with dense packing of biological building blocks. To further scale up the size of life forms that can be visually explored, the rendering pipeline needs to integrate runtime construction of the biological structure. Assembly rules define how a body of a certain biological entity is composed. Such rules need to be applied on-the-fly, depending on where the viewer is currently located in the 3D scene to generate full structural detail for that part of the scene. We will review how to construct membrane-like structures, soluble protein distributions, and fiber strands through parallel algorithms, resulting in a collision-free biologically-valid scene. Assembly rules that define how a life form is structurally built need to be expressed in an intuitive way for the domain scientist, possibly directly in the three-dimensional space. Instead of placing one biological element next to another one for the entire biological structure by the modelers themselves, only assembly rules need to be specified and the algorithm will complete the application of those rules to form the entire biological entity. These rules are derived from current scientific knowledge and from all available experimental observations. The Cryo-EM Tomography is on rising and shows that we can already now reach the near-atomistic detail when employing smart algorithms. Our assembly rules extraction, therefore, needs to integrate with microscopic observations, to create an atomistic representation of specific, observed life forms, instead of generic models thereof. Such models then can be used in whole-cell simulations, and in the context of automated science dissemination.
• 

  Recent developments for stochastic processes and their applications

  Ahmed Kebaier, Associate Professor, Laboratory Analysis, Geometry and Application (LAGA), University Sorbonne Paris Nord (USPN)

  Feb 16, 12:00 - 13:00

  KAUST

  stochastic

  In this talk, we present our theoretical results recently developed around various types of applications. These results cover asymptotic and non-asymptotic analysis, functional limit theorems, concentration inequalities, and Malliavin calculus techniques elaborated for several classes of stochastic processes. We showcase through concrete examples the role of these results in taking up various challenges at the cutting-edge of modern applied probability.
• 

  High-dimensional inference for max-stable processes

  Dr. Boris Beranger, Lecturer in Statistics, University of New South Wales, Sydney

  Feb 16, 11:00 - 12:00

  KAUST

  extreme environmental events Max-stable processes

  Droughts, high temperatures and strong winds are key causes of the recent bushfires that have touched a major part of the Australian territory. Such extreme events seem to appear with increasing frequency, creating an urgent need to better understand the behavior of extreme environmental phenomena.
• 

  Existence theory for non-separable mean field games - Session 3

  David Ambrose, Professor, Department of Mathematics at Drexel University, USA

  Feb 15, 17:00 - 19:00

  KAUST

  The time-dependent PDE system for mean field games is a coupled pair of parabolic equations, one forward in time and the other backward in time. The lecturer will demonstrate two techniques for proving existence and uniqueness of solutions for this system. The first of these techniques is inspired by work in fluid dynamics, as a similar forward-backward structure for vortex sheets was discovered by Duchon and Robert in the 1980s. Adapting the ideas of Duchon and Robert gives existence and uniqueness of solutions for the time-dependent mean field games system in function spaces based on the Wiener algebra. The second technique to be demonstrated by the speaker uses Sobolev spaces, and is an adaptation of the energy method to the forward-backward setting.
• 

  Resilience is the Cybersecurity of the XXI century, and here's why! - 2021-02-15

  Feb 15, 12:00 - 13:00

  KAUST

  In a nutshell, Resilient Computing is a new paradigm based on modelling, architecting and designing computer systems so that: they have built-in baseline defences; such defences cope with virtually any quality of threat, be it accidental faults, design errors, cyber-attacks, or unexpected operating conditions; provide incremental protection of, and automatically adapt to, a dynamic range of threat severity; provide sustainable operation.

Feb 7 - Feb 13, 2021

• 

  Visible Light Communications Technology and Machine Learning - 2021-02-12

  Dr. Paul Anthony Haigh, Newcastle University, United Kingdom and Dr. Bo Tan, Tampere University, Finland

  Feb 12, 16:00 - 17:15

  KAUST

  vlc

  In this webinar we will give an overview of several key technologies related to our special issues in Frontiers of Communications and Networks; (1) visible light communications (VLC) and (2) applications of machine learning in optical and wireless communication systems.
• 

  Proper scoring rules and model selection for stochastic processes

  David Bolin, Professor, Statistics

  Feb 11, 12:00 - 13:00

  KAUST

  In this talk, we begin by a brief introduction to proper scoring rules and their use in statistics. Then, we discuss an often overlooked problem: the up-weighting of observations with large uncertainty, which can lead to unintuitive rankings of models, by some of the most popular proper scoring rules, such as the continuously ranked probability score (CRPS), the MAE, and the MSE.
• 

  High Order Numerical Methods for Acoustic and Elastic Scattering

  Prof. Vianey Villamizar, Department of Mathematics, Brigham Young University, USA

  Feb 9, 14:00 - 15:00

  KAUST

  A focus of my current research is to develop numerical methods for the accurate simulation of sound and seismic waves. This waves usually occur in very large domains. For the computational purpose, the original unbounded wave problems need to be transformed into bounded ones. To accomplish this goal artificial boundaries are chosen to truncate the infinite domains. The challenge is to define absorbing boundary conditions (ABC) on these artificial boundaries such that the solutions of the new bounded problems approximate to a reasonable degree the solutions of the original unbounded problems in their common domains. In part, my efforts and those of my students and collaborators have been in deriving computational advantageous ABC and the formulation of a high order and high accurate numerical methods inside the truncated domain. In this talk, I will describe in some details our work. Also, I will include numerical results which demonstrate the improved accuracy and simplicity of our formulation when compared with commonly used ABC.
• 

  Existence theory for non-separable mean field games - Session 2

  David Ambrose, Professor, Department of Mathematics at Drexel University, USA

  Feb 8, 17:00 - 19:00

  KAUST

  The time-dependent PDE system for mean field games is a coupled pair of parabolic equations, one forward in time and the other backward in time. The lecturer will demonstrate two techniques for proving existence and uniqueness of solutions for this system. The first of these techniques is inspired by work in fluid dynamics, as a similar forward-backward structure for vortex sheets was discovered by Duchon and Robert in the 1980s. Adapting the ideas of Duchon and Robert gives existence and uniqueness of solutions for the time-dependent mean field games system in function spaces based on the Wiener algebra. The second technique to be demonstrated by the speaker uses Sobolev spaces, and is an adaptation of the energy method to the forward-backward setting.
• 

  Generative Adversarial Networks and Generative Modeling

  Peter Wonka, Professor, Computer Science

  Feb 8, 12:00 - 13:00

  KAUST

  I will review some background of generative adversarial modeling using GANs and discuss how our group has been using GANs for image editing.
• 

  Statistical models for studying biodiversity: challenges and contributions

  Antik Chakraborty, Postdoc, Department of Statistical Science, Duke University

  Feb 7, 16:00 - 17:00

  KAUST

  biodiversity

  A healthy ecosystem is essential to our well being and to ensure that we need a better understanding of the life or biodiversity around us. In the first part of this talk, I will introduce how modern technology is being used to collect data on biodiversity across space and time. These data come in complex forms such as DNA sequences, high-dimensional binary vectors, sound signals, etc. I will briefly touch upon the statistical challenges involved in making sense of these data. In the second part, I will elaborate on a project motivated by studying temporal patterns in bird vocalizations. I will introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed class of FRActional Probit (FRAP) models are based on the thresholding of a latent process consisting of an additive expansion of a smooth Gaussian process with a fractional Brownian motion. I will describe a Bayesian approach to inference using Markov chain Monte Carlo. Results from applying the model on Amazon bird vocalization data will be presented which provide substantial evidence for self-similarity and non-Markovian/Poisson dynamics. A hierarchical extension of the proposed model to accommodate vocalizations of multiple birds at the same time will also be discussed.