Events

Apr 18 - Apr 24, 2021

• 

  KL Divergence Regularized Learning Model for Multi-Agent Decision Making

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Apr 18, 12:00 - 13:00

  KAUST

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

Apr 11 - Apr 17, 2021

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  Introduction to hybrid dynamical systems: Part 1B

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

  Apr 15, 14:00 - 15:30

  KAUST

  Dynamical Systems Modelization Time Scale Approach

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

  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2021

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Apr 15, 12:00 - 13:00

  KAUST

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects, (iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria. The considered applications include optimization of decision trees and decision rule systems as algorithms for problem-solving, as ways for knowledge representation, and as classifiers, optimization of element partition trees for rectangular meshes which are used in finite element methods for solving PDEs, and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths.
• 

  Introduction to hybrid dynamical systems: Part 1A

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

  Apr 15, 10:30 - 12:00

  KAUST

  Dynamical Systems Modelization Time Scale Approach

  In recent years, new theoretical tools have been developed to describe complex systems more precisely, such as hybrid dynamical systems (HDS). Many works ranging from modelling to stabilisation, or control and observation have focused on the study of this class of systems.
• 

  Continuous-time optimal control on discrete spaces. Applications to inventory management in commerce and finance - Session 2

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

  Apr 13, 15:00 - 18:00

  KAUST

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

  Ingesting and Processing Data Efficiently for Machine Learning

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

  Apr 12, 12:00 - 13:00

  KAUST

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

  Pointing and Trajectory Tracking Control for Optical Wireless Communication

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

  Apr 11, 12:00 - 13:00

  KAUST

  Pointing Trajectory tracking control Optical wireless communication

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

Apr 4 - Apr 10, 2021

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

  Xin Gao, Program Chair, Computer Science

  Apr 8, 12:00 - 13:00

  KAUST

  COVID-19

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

  Discriminant Analysis and Support Vector Regression in High Dimensions: Sharp Performance Analysis and Optimal Designs

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

  Apr 8, 11:00 - 13:00

  KAUST

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

  Continuous-time optimal control on discrete spaces. Applications to inventory management in commerce and finance - Session 1

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

  Apr 6, 15:00 - 18:00

  KAUST

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

  Challenges of Differentially Private Empirical Risk Minimization

  Di Wang, Assistant Professor, Computer Science

  Apr 5, 12:00 - 13:00

  KAUST

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

  Blue LED material beyond Blue for Micro-LED Displays

  Kazuhiro Ohkawa, Professor (former), Electrical and Computer Engineering

  Apr 4, 12:00 - 13:00

  KAUST

  LED material Displays

  Energy is an indispensable part of our lives. We are challenging energy-saving novel light emitters and clean-energy generation systems at Energy Conversion Devices and Materials (ECO Devices) Laboratory at KAUST. The former is based on MOCVD technology, material science, and device technology. The latter is the nitride photocatalyst invented by Ohkawa. The development of highly-efficient InGaN-based blue LEDs was the topic of the 2014 Nobel Prize in Physics. InGaN-based green LEDs were realized after improving the quality of higher-In-content InGaN. The three primary colors in light are RGB. The current red LEDs are based on InGaP as the active region. If we realize red LEDs by InGaN, we can fabricate the monolithic RGB LEDs in a wafer. Such RGB integration will be a breakthrough for micro-LED displays that are the next generation after the OLED displays. In this seminar, the science of MOCVD, the growth of high-In-content InGaN, and the state-of-the-art InGaN-based red LEDs will be introduced.

Mar 28 - Apr 3, 2021

• 

  Safe autonomy with imperfect information

  Necmiye Ozay, Associate Professor, University of Michigan

  Apr 1, 22:00 - 22:45

  KAUST

  Featured Sessions

  RobotoKAUST safe autonomy invariance verification

  For more information please visit the website
• 

  Safe, Interaction-Aware Decision Making and Control for Robot Autonomy

  Marco Pavone, Associate Professor, Aeronautics and Astronautics, Stanford University

  Apr 1, 21:15 - 22:15

  KAUST

  Featured Sessions

  RobotoKAUST human-robot interactions decision making autonomous driving

  For more information please visit the website
• 

  Certification of Autonomous Vehicles: A Regulator-Oriented Approach

  Huan Xu, Assistant Professor, University of Maryland

  Apr 1, 20:30 - 21:15

  KAUST

  Featured Sessions

  RobotoKAUST Aerospace autonomy Certification regulations

  For more information please visit the website
• 

  Efficient Reachability for Safe Autonomous Systems: A Mixed Monotone Approach

  Sam Coogan, Assistant Professor, Georgia Institute of Technology

  Apr 1, 19:45 - 20:30

  KAUST

  Featured Sessions

  RobotoKAUST safe autonomy reachability invariance

  For more information please visit the website
• 

  The challenging path to creating fully autonomous and controllable microrobots

  Prof. Néstor O. Pérez-Arancibia, Autonomous Microrobotic Systems Laboratory, University of Southern California (USC)

  Apr 1, 18:45 - 19:30

  KAUST

  Featured Sessions

  RobotoKAUST microrobots autonomy Power Catalytic Artificial Muscles Flight Control Crawling Control

  For more information please visit the website
• 

  An Egocentric Wearable Camera for Assistive Technologies

  Mohammed Kutbi, Assistant Professor, Saudi Electronic University

  Apr 1, 18:00 - 18:45

  KAUST

  Featured Sessions

  RobotoKAUST Assistive Technology Egocentric Camera Wheelchair

  For more information please visit the website
• 

  Designing And Modelling Soft Active Artificial Muscles “Geometric and Material Nonlinearities”

  Marwa Eldiwiny, PhD Student, IEEE RAS Org

  Apr 1, 17:15 - 18:00

  KAUST

  Featured Sessions

  RobotoKAUST FEMSimulation SoftActiveMaterial Geometricandmaterial nonlinearities Controller design

  For more information please visit the website
• 

  The Robots are Coming – to your Farm

  Girish Chowdhary, Associate Professor, UIUC

  Apr 1, 16:15 - 17:00

  KAUST

  Featured Sessions

  RobotoKAUST robotics agriculture artificial intelligence

  For more information please visit the website
• 

  Wireless Magnetic Sensors to Empower the Next Technological Revolution

  Abdullah Almansouri, PhD Student, Electrical and Computer Engineering, KAUST

  Apr 1, 16:00 - 17:00

  KAUST

  The next technological revolution, Industry 4.0, is envisioned as a digitally connected ecosystem where machines and gadgets are driven by artificial intelligence. By 2025, more than 75 billion devices are projected to serve this revolution. Many of which are to be integrated into the fabrics of everyday life in the form of smart wireless sensors. Still, two major challenges should be addressed to realize truly wireless and wearable sensors. First, the sensors should be flexible and stretchable, allowing for comfortable wearing. Second, the electronics should scavenge the energy it requires entirely from the environment, thus, eliminating the need for batteries, which are bulky, create ecological problems, etc. By addressing these two challenges, this dissertation paves the way for truly wearable sensors.
• 

  On the Fundamental Challenges for Self-Driving Cars

  Emilio Frazzoli, Professor, Dynamic Systems and Control, ETH Zürich

  Apr 1, 15:30 - 16:15

  KAUST

  Featured Sessions

  RobotoKAUST autonomous vehicles Future Mobility

  For more information please visit the website
• 

  Distributed AI: Optimal algorithms for distributed stochastic non-convex optimization

  Usman A. Khan, Associate Professor, Tufts University

  Apr 1, 14:45 - 15:30

  KAUST

  Featured Sessions

  RobotoKAUST Multi-agent systems Nonconvex problems stochastic optimization

  For more information please visit the website
• 

  Swarms for People

  Sabine Hauert, Associate Professor, Bristol Robotics Laboratory, University of Bristol

  Apr 1, 14:00 - 14:45

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Levering AI for Unmanned Aerial Systems: Deployment Strategies

  Anis Koubaa, Professor, Prince Sultan University

  Apr 1, 13:15 - 14:00

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  From one to many, coordination problems of multiple mobile robots

  Lucia Pallottino, Associate Professor, University of Pisa

  Apr 1, 12:30 - 13:15

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Waves Functional Materials - 2021-04-01

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Apr 1, 12:00 - 13:00

  KAUST

  Wave functional materials are artificial materials that can control wave propagation as wish. In this talk, I will give a brief review on the progress of wave functional materials and reveal the secret behind the engineering of these materials to achieve desired properties. In particular, I will focus on our contributions on metamaterials and metasurfaces. I will introduce the development of effective medium, a powerful tool in modeling wave functional materials, followed by some illustrative examples demonstrating the intriguing properties, such as redirection, emission rate enhancement, wave steering and cloaking.
• 

  Multi-Robot Task Allocation Games in Dynamically Changing Environments with Application to Trash Collection

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering, KAUST

  Apr 1, 11:45 - 12:30

  KAUST

  Featured Sessions

  RobotoKAUST Multi-Robot Learning/Coordination Multi-Agent Decision Making game theory

  For more information please visit the website
• 

  Autonomous Vision-Based Navigation and Control for Robotic Space Objects

  Hesham Shageer, Assistant Professor, King Abdulaziz City for Science and Technology KACST

  Mar 31, 21:15 - 22:00

  KAUST

  Featured Sessions

  RobotoKAUST Autonomous Space Robotics Distributed Space Systems Navigation and Control Flying Robots

  For more information please visit the website
• 

  Optimization-Based Control of Autonomous Aerospace Systems

  Behcet Acikmese, Professor, William E. Boeing Department of Aeronautics and Astronautics, University of Washington, USA

  Mar 31, 20:30 - 21:15

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Xwing: the journey to deploying autonomous cargo aircraft

  Maxime Gariel, Chief Technology Officer, Xwing

  Mar 31, 19:45 - 20:30

  KAUST

  Featured Sessions

  RobotoKAUST autonomy automation Airspace Integration Xwing Cessna Caravan

  For more information please visit the website
• 

  Dynamic Explorations of a TiltWing Aircraft Transitioning from Conventional to Hovering Flight

  John Hauser, Professor, University of Colorado Boulder

  Mar 31, 19:00 - 19:45

  KAUST

  Featured Sessions

  RobotoKAUST Aircraft Maneuvering Slipstream Dynamic Pressure Distributed Electric Propulsion Nonlinear Maneuvering

  For more information please visit the website
• 

  Safe learning and control with L1 adaptation

  Naira Hovakimyan, Professor, Mechanical Science and Engineering, University of Illinois

  Mar 31, 18:15 - 19:00

  KAUST

  Featured Sessions

  RobotoKAUST

  For more information please visit the website
• 

  Core Challenges in Navigation in Human Environments

  Jean Oh, Senior Systems Scientist, Robotics Institute, Carnegie Mellon University

  Mar 31, 16:30 - 17:15

  KAUST

  Featured Sessions

  RobotoKAUST Social robot navigation motion planning motion prediction human-robot interactions

  For more information please visit the website
• 

  Transient Analysis of Electromagnetic and Acoustic Scattering using Second-kind Surface Integral Equations

  Rui Chen, PhD Student, Electrical and Computer Engineering, KAUST

  Mar 31, 16:00 - 18:00

  KAUST

  Time-domain methods are preferred over their frequency-domain counterparts for solving acoustic and electromagnetic scattering problems since they can produce wide-band data from a single simulation. Among the time-domain methods, time-domain surface integral equation solvers have recently found widespread use because they offer several benefits over differential equation solvers. This dissertation develops several second-kind surface integral equation solvers for analyzing transient acoustic scattering from rigid and penetrable objects and transient electromagnetic scattering from perfect electrically conducting and dielectric objects. For acoustically rigid, perfect electrically conducting, and dielectric scatterers, fully explicit marching-on-in-time schemes are developed for solving time domain Kirchhoff, magnetic field, and scalar potential integral equations, respectively.
• 

  Toward Improving the Internet of Things: Quality of Service and Fault Tolerance Perspectives

  Maha Al-Aslani, PhD Student, Computer Science, KAUST

  Mar 31, 16:00 - 17:00

  KAUST

  In this thesis defense, I will explore the unique characteristics of IoT traffic and examine IoT systems. The work is motivated by the new capabilities offered by modern Software Defined Networks (SDN) and blockchain technology. We evaluate IoT Quality of Service (QoS) in traditional networking. We obtain mathematical expressions to calculate end-to-end delay, and dropping. Then, we analyze IoT traffic load and propose an intelligent edge that can identify volumetric traffic and address them in real-time using an instantaneous detection method for IoT applications (IDIoT). This approach can easily detect a large surge and potential variation in traffic patterns for an IoT application, which may contribute to safer and more efficient operation of the overall system. Our results provide insight into the advantages of an intelligent edge serving as a detection mechanism.
• 

  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

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  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.
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  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.