Events

Aug 30 - Sep 6, 2020

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  Large-Scale Visualization of Scientific Data: Volume Data and Flow Fields - 2020-08-31

  Markus Hadwiger, Professor, Computer Science

  Aug 31, 12:00 - 13:00

  KAUST

  ‌ Abstract Welcome to the new semester! 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

Aug 23 - Sep 6, 2020

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  Power Electronics Applications in Offshore Wind Farms

  Otavio Bertozzi, Ph.D. Student, Electrical and Computer Engineering

  Aug 30, 12:00 - 13:00

  KAUST

  Power electronics Applications Offshore Wind Farms

  Advances in power electronics have enabled many renewable energy applications. Wind energy harnessing is very promising and offshore farm installations have grown considerably in the past years. In this seminar we will go through some of the fundamentals of these enabling technologies and their applications. We will also present a simple, reliable, efficient and cost-effective concept applied to parallel connection of offshore wind turbines.

Aug 9 - Aug 16, 2020

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  Semi-talk: Efficiency Improvement of AlGaN based Deep UV emitter grown by HT-MOCVD

  Dr. Byeongchan So, Postdoctoral researcher / Ph.D. Department of Nano-Semiconductor Engineering, ​Korea Polytechnic University

  Aug 13, 15:00 - 16:00

  KAUST

  In this seminar, the approaches for improving the efficiency of AlGaN based DUV emitters will be presented. The high-temperature metal organic chemical vapor deposition system has been used to grow high-quality AlGaN based epi-layers and nanostructure on the sapphire substrate.

Jul 26 - Aug 2, 2020

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  Additively Manufactured Vanadium Dioxide (VO₂) based Radio Frequency Switches and Reconfigurable Components

  Shuai Yang, Ph.D., Electrical and Computer Engineering

  Jul 30, 18:00 - 20:00

  KAUST

  Frequency-reconfigurable RF components are highly desired in a wireless system because a single frequency-reconfigurable RF component can replace multiple RF components to reduce the size, cost, and weight. Typically, the reconfigurable RF components are realized using capacitive varactors, PIN diodes, or MEMS switches, which are expensive, require tedious soldering steps, and are rigid and thus non-compatible with futuristic applications of flexible and wearable electronics. In this work, we have demonstrated vanadium dioxide (VO₂) based RF switches that have been realized through additive manufacturing technologies (inkjet printing and screen printing), which dramatically brings the cost down to a few cents. Also, no soldering or additional attachment step is required as the switch can be simply printed on the RF component. The printed VO₂ switches are configured in two types (shunt configuration and series configuration) where both types have been characterized with two activation mechanisms (thermal activation and electrical activation) up to 40 GHz. The measured insertion loss of 1-3 dB, isolation of 20-30 dB, and switching speed of 400 ns is comparable to other non-printed and expensive RF switches. Moreover, as an application for the printed VO₂ switches, a fully printed frequency reconfigurable filter has also been designed in this work.
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  Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

  Filip Hanzely, Ph.D., Applied Mathematics and Computational Sciences

  Jul 30, 16:00 - 18:00

  KAUST

  Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used to formulate these often ill-conditioned optimization tasks, there is a need for new efficient algorithms able to cope with these challenges. In this thesis, we deal with each of these sources of difficulty in a different way. To efficiently address the big data issue, we develop new methods which in each iteration examine a small random subset of the training data only. To handle the big model issue, we develop methods which in each iteration update a random subset of the model parameters only. Finally, to deal with ill-conditioned problems, we devise methods that incorporate either higher-order information or Nesterov's acceleration/momentum. In all cases, randomness is viewed as a powerful algorithmic tool that we tune, both in theory and in experiments, to achieve the best results.

Jul 19 - Jul 26, 2020

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  Semi-talk: Ion beams in III-nitride research: Doping, characterisation and radiation resistant electronics

  Prof. Katharina Lorenz, Instituto Superior Técnico, University of Lisbon

  Jul 23, 16:00 - 17:30

  KAUST

  Katharina Lorenz's main research interests are the doping of WBS with optically active ions and the study of radiation effects in WBS materials for radiation detectors and radiation resistant electronics.

Jul 12 - Jul 19, 2020

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  Semi-talk: Recent advances in deep-UV photodetectors based on ultrawide band gap semiconductors

  Prof. Digbijoy Nath, Indian Institute of Science

  Jul 16, 14:30 - 16:00

  KAUST

  Brief Biography Education: PhD, Electrical Engineering, 2013, The Ohio State University, Columbus, USA. B. E. (Hons), Electrical & Electronics Engineering, 2008, Birla Institute of Technology and Science (BITS), Pilani.
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  The Interplay between Mean-Field Games and Machine-Learning (Session III)

  Prof. Levon Nurbekyan, Department of Mathematics, UCLA

  Jul 15, 21:00 - 23:00

  KAUST

  In this short course, I will discuss connections between mean-field games (MFG) systems and modern machine-learning (ML) techniques and problems. In the first part of the course, roughly the first two lectures, I will present how various ML techniques can be applied to solve high-dimensional MFG systems that are far out of reach for traditional methods. In the second part of the course, I will discuss the reverse relation, namely, how the MFG framework can be useful for solving specific ML problems.
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  The Interplay between Mean-Field Games and Machine-Learning (Session II)

  Prof. Levon Nurbekyan, Department of Mathematics, UCLA

  Jul 14, 21:00 - 23:00

  KAUST

  In this short course, I will discuss connections between mean-field games (MFG) systems and modern machine-learning (ML) techniques and problems. In the first part of the course, roughly the first two lectures, I will present how various ML techniques can be applied to solve high-dimensional MFG systems that are far out of reach for traditional methods. In the second part of the course, I will discuss the reverse relation, namely, how the MFG framework can be useful for solving specific ML problems.
• 

  The Interplay between Mean-Field Games and Machine-Learning (Session I)

  Prof. Levon Nurbekyan, Department of Mathematics, UCLA

  Jul 13, 21:00 - 23:00

  KAUST

  In this short course, I will discuss connections between mean-field games (MFG) systems and modern machine-learning (ML) techniques and problems. In the first part of the course, roughly the first two lectures, I will present how various ML techniques can be applied to solve high-dimensional MFG systems that are far out of reach for traditional methods. In the second part of the course, I will discuss the reverse relation, namely, how the MFG framework can be useful for solving specific ML problems.

Jul 5 - Jul 12, 2020

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  Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems

  Tariq Alturkestani, Ph.D. Student, Computer Science

  Jul 9, 16:00 - 17:00

  KAUST

  Out-of-Core simulation systems often produce a massive amount of data that cannot fit on the aggregate fast memory of the compute nodes, and they also require to read back these data for computation. As a result, I/O data movement can be a bottleneck in large-scale simulations. Advances in memory architecture have made it feasible to integrate hierarchical storage media on large-scale systems, starting from the traditional Parallel File Systems to intermediate fast disk technologies (e.g., node-local and remote-shared NVMe and SSD-based Burst Buffers) and up to CPU’s main memory and GPU’s High Bandwidth Memory. However, while adding additional and faster storage media increases I/O bandwidth, it pressures the CPU, as it becomes responsible for managing and moving data between these layers of storage. Simulation systems are thus vulnerable to being blocked by I/O operations. The Multilayer Buffer System (MLBS) proposed in this research demonstrates a general method for overlapping I/O with computation that helps to ameliorate the strain on the processors through asynchronous access. The main idea consists in decoupling I/O operations from computational phases using dedicated hardware resources to perform expensive context switches. By continually prefetching up and down across all hardware layers of the memory/storage subsystems, MLBS transforms the original I/O-bound behavior of evaluated applications and shifts it closer to a memory-bound or compute-bound regime.
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  Heterogeneous Integration Strategy for Obtaining Physically Flexible 3D Compliant Electronic Systems

  Sohail Faizan Shaikh, Ph.D. Student, Electrical and Computer Engineering

  Jul 9, 15:00 - 16:00

  KAUST

  In this thesis, we present a pragmatic heterogeneous integration strategy to obtain high-performance 3D electronic systems using existing CMOS technology. Critical challenges addressed during the process are; reliable flexible interconnects, maximum area efficiency, soft-polymeric packaging, and heterogeneous integration compatible with current CMOS technology. First, a modular LEGO approach presents a novel method to obtain flexible electronics in a lock-and-key (plug and play) manner with reliable interconnects. It includes a process to convert standard rigid IC into flexible LEGO without any performance degradation with a high-yield. For the majority of healthcare and environmental monitoring applications, a sensory array is essential for continuous spatiotemporal activity recording. Here we present an ultra- high-density sensory solution (1 million sensors) as an epitome of density and address each of the associated challenges. A generic heterogeneous integration scheme is devised to obtain a physically flexible standalone electronic system using 3D-coin architecture. Lastly, a feather-light non-invasive ‘Marine-Skin’ platform to monitor deep-ocean monitoring is presented using the heterogeneous integration scheme. Electrical and mechanical characterization establish the reliability, integrity, robustness, and ruggedness of the processes, sensors, and multisensory flexible system.
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  Semi-talk: Characteristics of gallium oxide based wide bandgap semiconductors

  Prof. Qixin Guo, Department of Electrical and Electronic Engineering, Saga University

  Jul 9, 09:00 - 10:30

  KAUST

  Prof. Dr. Guo received B. E., M.E., and Dr. E degrees in electronic engineering from Toyohashi University of Technology in 1990, 1992, and 1996, respectively. He is currently a Professor of Department of Electrical and Electronic Engineering, Saga University as well as Director of Saga University Synchrotron Light Application Center. His research interests include epitaxial growth and characterization of semiconductor materials. Prof. Guo has published more than 300 papers in scientific journals including Nature Communications, Advanced Materials, Physical Review B, and Applied Physics Letters with more than 7200 citations (h-index: 43).
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  The Lagrangian Approach to First Order Mean Field Games (Session II)

  Prof. Francisco J. Silva, Applied Mathematics, University of Limoges, France

  Jul 8, 14:00 - 17:00

  KAUST

  In this course, we will consider the so-called Lagrangian approach to mean-field games. We will introduce the problem by recalling some basic results for nonatomic games in a static framework. Next, based on recent results in collaboration with Markus Fischer (University of Padua), I will introduce the analogous setting in the deterministic and dynamic framework. In the second part of the course, I will present the details of a convergence result, obtained in collaboration with Saeed Hadikhanloo, of equilibria of a suitable sequence of discrete-time and finite mean-field games, as introduced by Gomes, Mohr, and Souza in 2010, to an equilibrium of the first-order mean-field game system. The convergence proof relies importantly on the Lagrangian formulation of equilibria.
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  The Lagrangian Approach to First Order Mean Field Games (Session I)

  Prof. Francisco J. Silva, Applied Mathematics, University of Limoges, France

  Jul 6, 14:00 - 17:00

  KAUST

  In this course, we will consider the so-called Lagrangian approach to mean-field games. We will introduce the problem by recalling some basic results for nonatomic games in a static framework. Next, based on recent results in collaboration with Markus Fischer (University of Padua), I will introduce the analogous setting in the deterministic and dynamic framework. In the second part of the course, I will present the details of a convergence result, obtained in collaboration with Saeed Hadikhanloo, of equilibria of a suitable sequence of discrete-time and finite mean-field games, as introduced by Gomes, Mohr, and Souza in 2010, to an equilibrium of the first-order mean-field game system. The convergence proof relies importantly on the Lagrangian formulation of equilibria.

Jun 28 - Jul 5, 2020

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  Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

  Chiheb Ben Hammouda, Ph.D., Applied Mathematics and Computational Sciences

  Jul 2, 14:00 - 16:00

  KAUST

  hierarchical approximation methods option pricing Computational biology

  In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.
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  Regularity Theory for MFG Systems (Session II)

  Prof. Marco Cirant, Department of Mathematics, University of Padova, Italy

  Jul 2, 09:00 - 12:00

  KAUST

  In this short course, I will address some regularity aspects in the theory of Mean-Field Games systems, with special emphasis on stationary and uniformly elliptic problems. I will first describe some regularity results for linear uniformly elliptic PDEs and semi-linear PDEs of Hamilton-Jacobi type. Then, I will show how to use these tools to prove the existence (and in some cases uniqueness) of solutions to MFG systems.
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  Two-Scale Homogenization and Numerical Methods for Stationary Mean-Field Games

  Xianjin Yang, Ph.D. Student, Applied Mathematics and Computational Sciences

  Jul 1, 16:00 - 18:00

  KAUST

  mean-field games

  Mean-field games (MFGs) study the behavior of rational and indistinguishable agents in a large population. Agents seek to minimize their cost based upon statistical information on the population's distribution. In this dissertation, we study the homogenization of a stationary first-order MFG and seek to find a numerical method to solve the homogenized problem. More precisely, we characterize the asymptotic behavior of a first-order stationary MFG with a periodically oscillating potential. Our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems. Moreover, we prove the existence and uniqueness of the solution to these limit problems. Next, we notice that the homogenized problem resembles the problem involving effective Hamiltonians and Mather measures, which arise in several problems, including homogenization of Hamilton--Jacobi equations, nonlinear control systems, and Aubry--Mather theory. Thus, we develop algorithms to solve the homogenized problem, effective Hamiltonians, and Mather measures.
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  Regularity Theory for MFG Systems (Session I)

  Prof. Marco Cirant, Department of Mathematics, University of Padova, Italy

  Jun 30, 09:00 - 12:00

  KAUST

  In this short course, I will address some regularity aspects in the theory of Mean-Field Games systems, with special emphasis on stationary and uniformly elliptic problems. I will first describe some regularity results for linear uniformly elliptic PDEs and semi-linear PDEs of Hamilton-Jacobi type. Then, I will show how to use these tools to prove the existence (and in some cases uniqueness) of solutions to MFG systems.
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  Semi-talk: Wearable Textile Electronics for Personalized Health Care

  Jun Chen, Department of Bioengineering, University of California Los Angeles

  Jun 29, 19:00 - 20:30

  KAUST

  Dr. Jun Chen is currently an assistant professor in the Department of Bioengineering, University of California, Los Angeles. His current research focuses on nanotechnology and bioelectronics for energy, sensing, environment and therapy applications in the form of smart textiles, wearables, and body area sensor networks.

Jun 21 - Jun 28, 2020

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  Mean Field Games with Local Coupling (Session II)

  Prof. Alessio Porretta, Mathematical Analysis, University of Rome Tor Vergata, Italy

  Jun 25, 14:00 - 17:00

  KAUST

  We introduce several PDE tools which are useful in the study of mean field game systems with local couplings. Due to the lack of regularity of solutions, refined compactness and renormalization arguments are needed for a general approach leading to existence and uniqueness results. If time is enough, congestion models will be treated by similar techniques.
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  Mean field games with local coupling (Session I)

  Prof. Alessio Porretta, Mathematical Analysis, University of Rome Tor Vergata

  Jun 22, 14:00 - 17:00

  KAUST

  We introduce several PDE tools which are useful in the study of mean field game systems with local couplings. Due to the lack of regularity of solutions, refined compactness and renormalization arguments are needed for a general approach leading to existence and uniqueness results. If time is enough, congestion models will be treated by similar techniques.

Jun 14 - Jun 21, 2020

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  Situational Awareness and Decision-Making Support for Crowd Management in Mina, A Grand Challenge Project Funded by the Ministry of Education

  Dr. Emad Felemban, Associate Professor in Computer engineering of Umm Al-Qura University

  Jun 17, 13:00 - 14:00

  KAUST

  Computer science communication engineering

  Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.
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  Semi-talk: MOCVD epitaxial growth and device fabrication of GaN-based laser diodes: from visible to ultraviolet

  Prof Ping Chen, Institute of Semiconductor, Chinese Academy of Sciences

  Jun 16, 16:00 - 17:00

  KAUST

  Dr. Ping Chen now works as a full Professor in the Institute of Semiconductors, Chinese Academy of Sciences (Beijing China). He received his bachelor’s degree of Physics from the University of Science and Technology of China (USTC) in 2003, and doctor’s degree of Microelectronics and Solid State Electronics from the Graduate School in University of Chinese Academy of Sciences in 2008. He worked in Georgia Institute of Technology (Atlanta, GA) as a Visiting Scholar from 2017 to 2019.

Jun 7 - Jun 14, 2020

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  Semi-talk: Low Cost Photonics

  Dr. Naresh Chand, Life Fellow of IEEE, Associate Vice President, Chapter Relations of the IEEE Photonics Society

  Jun 9, 16:00 - 17:30

  KAUST

  Dr. Naresh Chand is a Life Fellow of IEEE, Associate Vice President, Chapter Relations of the IEEE Photonics Society, and the Chair, Photonics Society, North Jersey Chapter. Dr. Naresh Chand was previously with US R&D Center of Huawei Technologies in NJ in 2011-2019 where he was working on developing low-cost advanced technologies for Ultra Broadband Optical Access Networks. Prior to this, he worked for BAE Systems (2003-11), Agere Systems and AT&T/Lucent Bell Laboratories (1986-2003), and Dept of Electronics, Government of India (1974-79).

May 31 - Jun 14, 2020

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  Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

  Sultan Albarakati, Director, KAUST Academy

  Jun 7, 16:00 - 18:00

  KAUST

  optimization

  In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.

May 31 - Jun 7, 2020

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  Semi-talk: Deep UV photodetectors based on Ga2O3 thin films grown on sapphire and muscovite mica

  Prof. Rajendra Singh, Indian Institute of Technology Delhi

  Jun 5, 16:00 - 17:30

  KAUST

  Dr. Rajendra Singh is presently a Professor at the Department of Physics, IIT Delhi. He did M.Sc. (Physics) from D.B.S. College, Dehra Dun (affiliated to H.N.B. Garhwal University) in 1995. After that he joined Inter University Accelerator Centre (formerly Nuclear Science Centre), New Delhi for Ph.D. His Ph.D. work was related to the study of the effect of swift heavy ion irradiation on electrical properties of Si and GaAs. He completed his Ph.D. in 2001 with degree from Jawaharlal Nehru University, New Delhi. He then joined Walter Schottky Institute (WSI), Technical University of Munich (TUM), Germany as a post doctoral fellow. There he worked on the design, fabrication and characterization of InP-based heterojunction bipolar transistors (HBTs). He extensively used Class 100 Cleanroom facilities at WSI working on various processing tools such as photolithography, wet etching, reactive ion etching, UHV metallization and rapid thermal annealing. In January 2004 he joined the Max Planck Institute of Microstructure Physics, Halle, Germany as a post doctoral fellow. There he worked in the area of direct wafer bonding and layer splitting of semiconductors for the fabrication of silicon-on-insulator (SOI) and strained silicon-on-insulator (sSOI). He worked in a Class 10 Cleanroom facility at MPI Halle using processing tools such as wet benches, wafer bonding system, plasma enhanced chemical vapour deposition (PECVD) and annealing furnaces.
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  Game-Theoretic Analysis of Smart Grid Markets: From Retail to Wholesale

  Khaled Alshehri, Assistant Professor, KFUPM

  Jun 4, 13:00 - 14:00

  KAUST

  game theory energy

  As end-consumers of electricity become more proactive and as many countries around the world push for a deeper penetration of renewable resources into the power grid, critical issues and challenges arise to the design and operation of deregulated electricity markets. In this presentation, we show how one can exploit tools from game theory to address some of these critical issues. Firstly, wholesale and retail markets are becoming more integrated due to the increasing adoption of distributed energy resources, creating a large gap in the current understanding of the impact of such small-scale energy resources on the larger power system operation and electricity market outcomes. This motivates us to develop a metric, called the Price of Aggregation, which quantifies the impact of integrating distributed energy resources in the retail-level on wholesale market efficiency. Secondly, evidence from real markets indicate that large-scale adoption of wind energy in the transmission system leads to significantly higher price volatility in wholesale markets. To mitigate the effects of price volatility, we propose an add-on centralized clearing mechanism that is applicable to any wholesale market, with the aim of allowing any market participant to hedge against profit volatilities, without changing the existing market operations. Finally, we develop a multiperiod-multicompany demand response framework in retail markets, which captures the behavior of competing companies and their price-responsive end-consumers. Using real-life data, we demonstrate potential savings that can exceed 30% for end-consumers, in addition to revealing desirable mathematical properties and deep insights.

May 24 - May 31, 2020

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  Semi-Talk: III-Nitride Nanowire Light-Emitting Diodes on Silicon and Patterned Substrates

  Prof Hieu Nguyen, Electrical and Computer Engineering, New Jersey Institute of Technology

  May 29, 18:00 - 19:00

  KAUST

  Prof. Hieu Nguyen received the B.S. degree in Physics from Vietnam National University in Ho Chi Minh City, Vietnam (2005), the M.S. degree in Electronics Engineering from Ajou University, South Korea (2009), and the PhD. degree in Electrical Engineering from McGill University, Canada (2012). In September 2014, he joined the Electrical and Computer Engineering Department, New Jersey Institute of Technology. He has authored/co-authored 1 book chapter, 1 patent, 48 journal articles, and more than 70 conference presentations.
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  Indoor 3D Scene Understanding Using Depth Sensors

  Jean Lahoud, Ph.D., Electrical and Computer Engineering

  May 28, 16:00 - 18:00

  KAUST

  Computer Vision 3D object detection Deep learning

  One of the main goals in computer vision is to achieve a human-like understanding of images. This understanding has been recently represented in various forms, including image classification, object detection, semantic segmentation, among many others. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.

May 17 - May 24, 2020

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  Semi-Talk: Role of 3D chemical imaging in advancing wide band gap technology

  Prof. Baishakhi Mazumder, Department of Materials Design and Innovation, School of Engineering and Applied Sciences, University at Buffalo

  May 22, 16:00 - 17:00

  KAUST

  No summary is available.

May 10 - May 17, 2020

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  Semi-Talk: III-Nitride Semiconductor Photonics and Nanoelectronics

  Prof. Jing Zhang, Electrical and Microelectronic Engineering, Rochester Institute of Technology

  May 15, 21:00 - 22:00

  KAUST

  Dr. Jing Zhang is currently the Kate Gleason Endowed Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology. She obtained B.S. degree in Electronic Science and Technology from Huazhong University of Science and Technology (2009), and Ph.D. degree in Electrical Engineering from Lehigh University (2013). Dr. Zhang’s research focuses on developing highly efficient III-Nitride and GaO semiconductor based photonic, optoelectronic, and electronic devices. Her research group is working on the development of novel quantum well active regions and substrates for enabling high-performance ultraviolet and visible LEDs/ lasers, as well as engineering of advanced device concepts for nanoelectronics.

May 3 - May 10, 2020

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  Modeling and Automated Storytelling of Mesoscale Molecular Models

  Ivan Viola, Professor, Computer Science

  May 4, 12:00 - 13:00

  KAUST

  multi-scale 3D visualization biological models

  In my talk, I will present techniques that allow biologists to model a mesocale entity in a rapid way in the timeframe of a few minutes to hours. This way we have created the first complete atomistic model of the SARS-CoV-2 virion that we are these days sharing with the worldwide scientific community. Mesoscale represents a scalar gap that is currently not possible to accurately image with neither microscopy nor X-ray crystallography approaches. For this purpose, scientists characterize it by observations from the surrounding nanoscale and the microscale. From this information, it is possible to reconstruct a three-dimensional model of a biological entity with a full atomistic model. The problem is that these models are enormously large and are not possible to model with traditional methods from computer graphics within a reasonable time.

Apr 26 - May 10, 2020

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  6G: Super-connecting the connected and connecting the unconnected

  Mohamed-Slim Alouini, Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering

  May 3, 12:00 - 13:00

  KAUST

  6G

  This talk aims to 1) provide an envisioned picture of 6G, 2) serve as a research guideline in the beyond 5G era, and 3) go over the recently proposed solutions to provide high-speed connectivity in under-covered areas to serve and contribute to the development of far-flung regions. The role of Internet and Communication Technology (ICT) in bringing about a revolution in almost all aspects of human life needs no introduction. It is indeed a well-known fact that the transmission of the information at a rapid pace has transformed all spheres of human life such as economy, education, and health to name a few. In this context, and as the standardization of the fifth generation (5G) of wireless communication systems (WCSs) has been completed, and 5G networks are in their early stage of deployment, the research visioning and planning of the sixth generation (6G) of WCSs are being initiated. 6G is expected to be the next focus in wireless communication and networking and aim to provide new superior communication services to meet the future hyper-connectivity demands in the 2030s.

Apr 26 - May 3, 2020

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  Semi-Talk: Multifunctional Flexible Semiconductor Materials and Devices for Photonic, Electronic, Energy, and Sensing Applications

  Prof. Jae-Hyun Ryou, Mechanical Engineering, Texas Center for Superconducitity

  May 1, 16:00 - 17:00

  KAUST

  Brief Biography Prof. Jae-Hyun Ryou is an Associate Professor of Mechanical Engineering Also affiliate with Materials Science and Engineering Program, Texas Center for Superconducitity (TcSUH), and Advanced Manufacturing Institute (AMI) Education and experience: Ph.D. in Materials Science and Engineering, University of Texas at Austin M.S. and B.S. in Metallurgical Engineering, Yonsei University, Seoul, Korea Honeywell Technology Center Center for Compound Semiconductors at Georgia Institute of Technology Research: Current research interests: semiconductor materials, devices, and
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  On Optimal Regularization in Estimation, Detection, and Classification

  Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

  Apr 30, 12:00 - 13:00

  KAUST

  linear systems computational Complexity Convex Gaussian Min-max theorem

  In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.
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  A New Approach to Quad Mesh Processing for Computational Design and Fabrication

  Apr 27, 12:00 - 13:00

  KAUST

  computational design fabrication

  In this talk, we will discuss a new way of computing with quad meshes. It is based on the checkerboard pattern of parallelograms one obtains by subdividing a quad mesh at its edge midpoints. The new approach is easy to understand and implement. It simplifies the transfer from the familiar theory of smooth surfaces to the discrete setting of quad meshes. This is illustrated with applications to constrained editing of 3D models, mesh design for architecture and digital modeling of shapes which can be fabricated by bending flat pieces of inextensible sheet material.

Apr 19 - May 3, 2020

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  Distributed Robotics

  Apr 26, 12:00 - 13:00

  KAUST

  robotics distributed systems

  This talk presents an overview of challenges, state-of the-art, and applications for distributed robotic systems. In distributed robotic systems, there is a group of robots that seek to achieve a collective task. Applications include environmental monitoring, search and rescue, and programmable self-assembly. Settings can range from a small team of cooperative robots to a swarm of many interacting agents. An essential feature of such systems is that individual robots make decisions based on available local information as well as limited communications with other robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized paradigm, both measurements and decisions are distributed among multiple actors.

Apr 19 - Apr 26, 2020

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  Efficient and Physics-Preserving Discretization Schemes for Multiphase Flow Problems

  Shuyu Sun, Professor, Earth Science and Engineering

  Apr 23, 12:00 - 13:00

  KAUST

  No summary is available.
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  Nanomaterials and Nanostructure Based Energy Efficient In-Memory Sensing

  Nazek El-Atab, Assistant Professor, Electrical and Computer Engineering

  Apr 20, 12:00 - 13:00

  KAUST

  nanomaterials Nanostructure energy efficiency

  In this talk, I will present the potential of nano-islands grown by the atomic layer deposition (ALD) in addition to novel material combinations in scaled memory devices. Moreover, in order to relax the scalability requirement, I will introduce an integrated potential single device solution by infusing memory device which can monitor and remember our actions and the surrounding environment. Such a device performs the roles of both a MEMory and a senSOR at once and is referred to as a MEMSOR. The MEMSOR is directly programmed by external physical stimuli rather than an applied electric potential as in conventional devices. As a result, the MEMSOR leads to faster data analysis, lower footprint area, energy consumption, and ideally, the cost of the system. Finally, the MEMSOR device will be based on a MOSFET architecture with a mature manufacturing process, and thus it can be integrated with conventional Flash devices on the same silicon wafer with minimal additional process steps.
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  Simulation of Neuronal Signal Processing - 2020-04-20

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Apr 20, 12:00 - 13:00

  KAUST

  Neuronal ensembles

  In the lecture, we present a three-dimensional model for the simulation of signal processing in neurons. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

Apr 12 - Apr 26, 2020

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  Near Field Wireless Power Transfer: Circuits and Application

  Shehab Ahmed, Professor, Electrical and Computer Engineering

  Apr 19, 12:00 - 13:00

  KAUST

  Near Field wireless Power Transfer

  Recently, wireless power transfer has been used in mobile phones, electric vehicles, medical implants, wireless sensor networks, unmanned aerial vehicles, and so on. In this seminar, we will walk through circuit theory and simulations that illustrate the nature and constraints of the technology. We will also address contemporary topics related to application of wireless power transfer in electric vehicle applications.

Apr 12 - Apr 19, 2020

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  Economic Model Predictive Control Strategies for Nonlinear Systems

  Fahad Albalawi , Research Scientist, Electrical and Computer Engineering

  Apr 16, 16:00 - 17:30

  KAUST

  Model Predictive Control neural network nonlinear control systems

  The first part of this talk provides a brief introduction of the EMPC (motivation, challenges, and solutions). The second part of this talk proposes a regret-based robust EMPC paradigm for nonlinear systems subject to unknown but bounded disturbance. The main motivation of the proposed work is the possible improvement of the economic performance when one considers the regret function as the objective function for the robust EMPC algorithm instead of the worst cost. The third part of this talk introduces an integrated framework that combines a Neural Network (NN) algorithm with an MPC scheme that can guarantee closed-loop stability in the presence of deception cyberattacks (e.g., min-max cyberattack). Both discrete-time and continuous-time nonlinear systems will be utilized throughout the talk to demonstrate the applicability and effectiveness of the proposed control methods. Finally, future research directions will be presented at the end of the talk.
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  Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

  Xin Gao, Program Chair, Computer Science

  Apr 16, 12:00 - 13:00

  KAUST

  transcription factor optimization Deep learning

  Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Apr 13, 12:00 - 13:00

  KAUST

  data mining Dynamic programming combinatorial optimization

  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.

Apr 5 - Apr 19, 2020

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  Evolutionary science via nonlinear orthography

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Apr 12, 12:00 - 13:00

  KAUST

  Evolutionary science nonlinear orthography

  The goal of our research is to understand the physical origin of these behaviors and trasform them into sustainable technologies that tackle contemporary problem of global interest, ranging from energy harvesting to clean water production, design of smart materials, biomedical applications, information security, artificial intelligence, global warming, and so on. Creating technologies from complex natural systems is a modern interdisciplinary research field that permeates many different scientific areas, ranging from physics to mathematics, to engineering and the theory of linguistics. This is a very challenging, yet very promising research. It involves the understanding of what we consider complex, which translates as something “involved, intricate, complicated, not easily understood or analyzed”. This sets the challenge of being able to understand the mechanisms of these systems and cross many different disciplines in order to constructively harness their properties into reproducible applications.

Apr 5 - Apr 12, 2020

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  Geometry and Computational Design

  Apr 9, 12:00 - 13:00

  KAUST

  Geometry computational design

  An important stream of research in computational design aims at digital tools which support users in realizing their design intent in a simple and intuitive way, while simultaneously taking care of key aspects of function and fabrication. Such tools are expected to shorten the product development cycle through a reduction of costly feedback loops between design, engineering and fabrication. The strong coupling between shape generation, function and fabrication is a rich source for the development of new geometric concepts, with an impact to the original applications as well as to geometric theory. This will be illustrated at hand of applications in architecture and fabrication with a mathematical focus on discrete differential geometry and geometric optimization problems.
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  Predicting Single Gene Loss of Function Phenotypes

  Maxat Kulmanov, Research Scientist, Bio-Ontology Research Group

  Apr 6, 19:30 - 21:30

  KAUST

  We developed and expanded novel methods for representation learning, predicting protein functions and their loss of function phenotypes. We use deep neural network algorithm and combine them with symbolic inference into neural-symbolic algorithms. Our work significantly improves previously developed methods for predicting protein functions through methodological advances in machine learning, incorporation of broader data types that may be predictive of functions, and improved systems for neural-symbolic integration. The methods we developed are generic and can be applied to other domains in which similar types of structured and unstructured information exist. In future, our methods can be applied to prediction of protein function for metagenomic samples in order to evaluate the potential for discovery of novel proteins of industrial value. Also our methods can be applied to the prediction of loss of function phenotypes in human genetics and incorporate the results in a variant prioritization tool that can be applied to diagnose patients with Mendelian disorders.
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  Computation of High-Dimensional Multivariate Normal and Student-t Probabilities Based on Matrix Compression Schemes

  Jian Cao, Ph.D., Statistics

  Apr 6, 16:00 - 18:00

  KAUST

  The thesis focuses on the computation of high-dimensional multivariate normal (MVN) and multivariate Student-t (MVT) probabilities. Firstly, a generalization of the conditioning method for MVN probabilities is proposed and combined with the hierarchical matrix representation. Next, I revisit the Quasi-Monte Carlo (QMC) method and improve the state-of-the-art QMC method for MVN probabilities with block reordering, resulting in a ten-time-speed improvement. The thesis proceeds to discuss a novel matrix compression scheme using Kronecker products. This novel matrix compression method has a memory footprint smaller than the hierarchical matrices by more than one order of magnitude. A Cholesky factorization algorithm is correspondingly designed and shown to accomplish the factorization in 1 million dimensions within 600 seconds. To make the computational methods for MVN probabilities more accessible, I introduce an R package that implements the methods developed in this thesis and show that the package is currently the most scalable package for computing MVN probabilities in R. Finally, as an application, I derive the posterior properties of the probit Gaussian random field and show that the R package I introduce makes the model selection and posterior prediction feasible in high dimensions.
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  Deep Continual Learning

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Apr 6, 12:00 - 13:00

  KAUST

  Deep learning

  In this seminar, I will present some of the work I have done on Continual Deep Learning, among the research topics at the Vision-CAIR group. Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity as adopted in modern deep learning techniques. Decreasing the gap towards human-level continual learning, we extended continual deep learning from multiple perspectives. The Hebb's learning theory from biology can be famously summarized as “Cells that fire together wire together.". Inspired by this theory from biology, we proposed Memory Aware Synapses (ECCV18) to quantify and reduce machine forgetting in a way that enables leveraging unlabeled data, which was not possible in former techniques. We later developed a Bayesian approach appearing at ICLR2020, where we explicitly modeled uncertainty parameters to orchestrates forgetting in continual learning. We showed in our ICLR2019 and ACCV18 works that task descriptors/ language can operate in continual learning visual tasks to improve learning efficiency and enable zero-shot task transfer. Beyond computer vision tasks, we recently developed an approach appearing at ICLR2020 we call "Compositional Language Continual Learning". We showed that disentangling syntax from semantics enables better compositional Seq2Seq learning and can significantly alleviate forgetting of tasks like machine translation. In the talk, I will go over these techniques and shed some light on future research possibilities.