Events

Jun 23 - Jun 29, 2019

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  5G IoT Networks: from Massive access to URLLC

  Prof. Yonghui Li, Director of Wireless Engineering Laboratory, The University of Sydney

  Jun 24, 16:30 - 17:30

  B1 L3 R3119

  the university of sydney IoT M2M Communication

  Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.
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  Analysis and Optimization of Massive MIMO Systems via Random Matrix Theory Approaches

  Ikram Boukhedimi, Ph.D., Electrical and Computer Engineering

  Jun 24, 12:00 - 14:00

  B1 L3 R3119

  Random Matrix Theory wireless communication systems Ultra-massive MIMO systems

  Massive multiple-input multiple-output (MIMO) is a key enabling technology to achieve the required spectral and energy efficiency of the next generation of wireless networks. By endowing the base station (BS) with hundreds of antennas and relying on spatial multiplexing, massive MIMO allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate massive MIMO in different contexts.
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  Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

  Khalil Elkhalil, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Jun 24, 09:00 - 10:00

  B1 L4 R4214

  Random Matrix Theory machine learning high dimensional statistics data science

  Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Jun 9 - Jun 15, 2019

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  Efficient Localization of Human Actions and Moments in Videos

  Victor Escorcia, Ph.D., Electrical and Computer Engineering

  Jun 11, 15:00 - 16:00

  B3 L5 R5220

  Computer Vision machine learning artificial intelligence

  Abstract We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want

May 26 - Jun 1, 2019

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  Quantum Inspired Photonics

  Prof. Liang Feng, Department of Materials Science and Engineering, University of Pennsylvania

  May 30, 10:00 - 11:00

  B1 L2 R4214

  University of Pennsylvania quantum mechanics photonics

  Quantum mechanics and photonics share mathematical equivalence. By carefully exploiting the interplay between optical index, gain and loss in the complex dielectric permittivity plane, optics has become an ideal platform to explore some exotic quantum concepts, such as topological physics and parity-time (PT) symmetry. Instead of counteracting optical losses at micro and nano scales in integrated photonics, we started from an opposite viewpoint and developed a new paradigm of positively and strategically manipulating optical losses by the quantum-inspired photonics concept enrich fundamental optical physics and realized novel photonic synthetic matters with unique optical functionalities. In this seminar, I will present our recent efforts on engineering the complex optical potentials at an exceptional point (i.e. PT symmetry transition point). Based on the exceptional point-induced unidirectionality, we harness optical losses to enable unique microlaser functionalities, in particular, an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. Additionally, I will discuss non-Hermitian topological photonics where optical non-Hermiticity and topological physics are coupled.
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  Light Matter Interactions in Layered Materials: from Trivial to Topological Band Structures

  Professor Ritesh Agarwal, Department of Materials Science and Engineering, University of Pennsylvania

  May 30, 09:00 - 10:00

  B1 L2 R4214

  University of Pennsylvania layered materials

  Strongly confined electrical, optical and thermal excitations drastically modify material’s properties and break local symmetries that can enable precisely tunable novel responses and new functionalities. We will discuss the effect of engineered plasmonic lattice on light matter interactions in 2D excitonic crystals to produce novel responses such as enhanced and tunable emission, Fano resonances and strong exciton-plasmon polaritons, which can be precisely controlled by geometry and applied fields to produce novel device concepts. Our recent work on collective polaritonic modes and the formation of a complete polaritonic bandgap in few-layered excitonic semiconductors coupled to plasmons will also be presented.
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  Part 4: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 29, 15:30 - 17:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 4: I will discuss the quantitative isoperimetric inequality in the general case and the quantitative form of related geometric and functional inequalities.
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  Modern Techniques of Statistical Optimization for Machine Learning

  Tong Zhang, Professor of Computer Science and Mathematics, HKUST

  May 29, 12:00 - 13:00

  B9 H1

  HKUST statistics nonconvex stochastic optimization machine learning

  Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.
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  Stationary Mean-Field Games with Congestion

  David Evangelista, Ph.D. Student, Applied Mathematics and Computational Sciences

  May 28, 14:00 - 15:00

  B2 L5 R5220

  mean-field games

  Mean-field games MFG are models of large populations of rational agents who seek to optimize an objective function that takes into account their state variables and the distribution of the state variable of the remaining agents. MFG with congestion model problems where the agents’ motion is hampered in high-density regions. First, we study radial solutions for first- and second-order stationary MFG with congestion on R^d. Next, we consider second-order stationary MFG with congestion and prove the existence of stationary solutions. Additionally, we study first-order stationary MFG with congestion with quadratic or power-like Hamiltonians.
• 

  Part 3: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 27, 14:30 - 16:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 3: I will present Fuglede’s proof of the quantitative isoperimetric inequality for convex and for nearly spherical sets
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  High Precision Comb-Assisted Molecular Spectroscopy in the Mid-Infrared

  Bidoor AlSaif, Ph.D. Student, Electrical and Computer Engineering

  May 27, 10:00 - 11:00

  B2 L5 R5220

  Optical sensors molecular spectroscopy

  The design of laser-based optical sensors relies heavily on precise spectroscopic knowledge of atomic and molecular absorption transitions. Accurate spectroscopic information is invaluable in several fields such as biology, chemistry, astronomy, and environmental science. Within the electromagnetic spectrum, the mid-infrared (MIR) region can enable sensors with higher sensitivity due to the stronger absorption cross-sections. Moreover, MIR spectral transitions correspond to the fundamental vibrational motions of the molecules and are thus considered fingerprints of the molecular structure. Vibrational bands contain many rotational transitions, resulting in fine-splitting of spectral bands, particularly in gaseous samples. In order to resolve the fine rotational structure of vibrational bands, high-resolution MIR spectrometers are needed.
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  Non-Orthogonal Multiple Access for 5G and Beyond

  Prof. Daniel Costa , Federal University of Ceará

  May 26, 14:00 - 15:00

  B1 L2 R2202

  Federal University of Ceará 5th generation cellular

  Non-orthogonal multiple access (NOMA) has recently emerged not only as a new design of multiple access techniques in cellular networks, but also as a general principle of network architecture for applications beyond cellular systems. This talk will present and discuss the fundamentals of NOMA, and examine how it can be combined with other emerging communication technologies. Some new research trends and challenges will also be discussed.

May 19 - May 25, 2019

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  Part 2: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 22, 14:30 - 16:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 2: I will present De Giorgi’s proof of the isoperimetric inequality and comment on other proofs of the isoperimetric inequality
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  A Uniquely Defined Digital Experience

  Jason E. Roos, Chief Information Officer, KAUST

  May 22, 08:30 - 09:30

  B9 H2

  digital experience KAUST community

  “KAUST Smart” is a newly launched program intended to facilitate the design and implementation of digital experiences that will be developed in partnership with leading technology companies and the tremendous talent of KAUST. These unique solutions will be piloted and tested utilizing the KAUST community as a living laboratory and showcased to the rest of the world as an example of how KAUST is a global innovation leader.
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  Numerical simulation in engineering and medicine - On multi-scale approaches, scientific software, and uncertainty

  Prof. Rolf Krause, Faculty of Informatics, Università della Svizzera Italiana, Lugano

  May 21, 12:00 - 13:00

  B9 H2

  Complex geometries, non-smooth surface-effects, non-linear and heterogeneous material behaviour, and coupled multi-physics problems pose fundamental challenges for numerical simulation methods - may it be on the mathematical or on the software sie. In this talk, we will present and discuss multi-scale approaches for the efficient solution of smooth and non-smooth large scale non-linear systems, and we will comment on how to realize them in modern and HPC-friendly scientific software. Particular emphasis will be put on the design and construction of non-standard approximation and model hierarchies for non-linear problems, which then will be exploited for designing efficient solution methods in space, time, and space-time.
• 

  Part 1: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 20, 14:30 - 16:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 1: I will introduce some basic notions of the theory of sets of ignite perimeter and of geometric measure theory.
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  Data mining and fusion for image analysis in remote sensing

  Dr. Wenzhi Liao, Ghent University, Belgium

  May 19, 12:00 - 13:00

  B2 L5 R5209

  Specifically, I will introduce hyperspectral image restoration and its impacts on content interpretation. Despite advances in sensor technologies, degradation (e.g., noise, blur, low resolution, etc.) cannot be avoided during the hyperspectral images’ acquisitions, which can affect information retrieval and content interpretation. The first part of my talk will present the techniques I developed to improve the image qualities (noise reduction, sharpening, resolution enhancement, etc.), with specific applications to plant disease mapping in precision agriculture and fruit bruise detection in food inspection.

May 12 - May 18, 2019

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  Numerical methods for stochastic conservation laws with multiplicative rough drivers in the flux

  Håkon A. Hoel, Visiting Researcher, Stochastic Numerics Research Group

  May 16, 15:00 - 16:00

  B1 L4 R4214

  numerical methods numerical analysis

  Abstract Stochastic conservation laws (SCL) with quasilinear multiplicative``rough'' path dependence in the flux arise in modeling of mean field games. An impressive collection of theoretical results has been developed for SCL in recent years by Gess, Lions, Perthame, and Souganidis. We present the first fully computable numerical methods for pathwise solutions of scalar SCL with, for instance, "rough" paths in the form of Wiener processes. Convergence rates are derived for the numerical methods and we show that for strictly convex flux functions, "rough" path oscillations lead to
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  The strength of multidisciplinary research: the joint example of statistics and geoscience

  Dr. Luigi Lombardo, University of Twente, Netherlands

  May 14, 16:00 - 17:00

  B1 L4 R4102

  University of Twente spatial statistics

  Different scientific branches have the potential to develop topics which would provide visibility and fame. However, comparable if not greater milestones can be achieved when researchers from totally different fields join their efforts. This seminar will summarize the scientific journey of a former member of KAUST, which spent three years here as a postdoc in statistics coming from a pure geological background, combining the best out of the two worlds. Examples of the latest researches will be provided in the context of space, time and space-time statistics, bridging it with the underlying geoscientific research questions.
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  Sim-to-Real Transfer for Autonomous Navigation

  Matthias Mueller, Ph.D., Electrical and Computer Engineering

  May 14, 16:00 - 17:00

  B2 L5 R5220

  Computer Vision UAV robotics machine learning

  This work investigates the problem of transfer from simulation to the real world in the context of autonomous navigation. To this end, we first present a photo-realistic training and evaluation simulator Sim4CV which enables several applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator features cars and unmanned aerial vehicles (UAVs) with a realistic physics simulation and diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning.
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  Adaptive Intrusion Management System for Databases

  Muhamad Felemban, Assistant Professor, Computer Engineering Department, KFUPM

  May 13, 12:00 - 13:00

  B9 L2 H1

  cybersecurity

  With the growing cyber-security threats to governmental and organizational infrastructures, the need to develop high resilient systems that preserve the security and privacy of data is becoming increasingly important. Although there is a large body of work on security and privacy countermeasures, cyber-attacks still persist. A prominent type of such attacks is intrusion attack that aims at data tampering, which can impair the availability and the integrity of data.

May 5 - May 11, 2019

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  Joint longitudinal-survival models using R-INLA

  Janet van Niekerk, Research Scientist, Statistics

  May 9, 12:00 - 13:00

  B9 L2 H1

  stochastic smoothing pipeline R-INLA

  Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.
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  A Storage-Optimal Convex Optimization Framework with Applications to Semidefinite Programming

  Alp Yurtsever, PhD Candidate, EPFL

  May 6, 12:00 - 13:00

  B9 L2 H2

  semidefinite programming convex optimization

  With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, there is a recent trend where heuristic approaches with unverifiable assumptions are overtaking more rigorous, conventional optimization methods at the expense of robustness. This trend can be overturned when we exploit dimensionality reduction at the core of optimization. I contend that even the classical convex optimization did not reach yet its limits of scalability.
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  Modelling on the Service of Cooperative Communications

  Prof. Slim Chaoui, Jouf University, KSA

  May 5, 14:00 - 15:00

  B1 L3 R3119

  jouf university Cooperative communications Rayleigh-Gaussian model

  I will highlight the main contributions in the field of coded cooperative communications, where at first a study on the performance analysis of network-coded distributed coding schemes with different strategies for handling the relay-error propagation problem will be presented. Furthermore, a study proposing a relay selection scheme based on network-coded soft information relaying will be presented. The introducing of the Rayleigh-Gaussian model, which is applied to the forwarded relay soft symbols, have shown ability to give better performance in dealing with error propagation, and allowed us to give a tractable performance analysis of network-coded schemes under Rayleigh fading channels.

Apr 28 - May 4, 2019

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  Part 3: Introduction to homogenisation and Gamma-Convergence

  Filippo Cagnetti, Senior Lecturer in Mathematics, University of Sussex, UK

  May 2, 14:00 - 16:00

  B1 L4 R4214

  gamma-convergence PDE

  We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 3: (1) Compactness; (2) Application of the compactness result; 93) The homogenisation theorem.
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  Estimating the expected information gain in Bayesian optimal experimental design

  May 2, 12:00 - 13:00

  B9 L2 H1

  optimization DLMC DLMCIS

  Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.
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  Part 2: Introduction to homogenisation and Gamma-Convergence

  Filippo Cagnetti, Senior Lecturer in Mathematics, University of Sussex, UK

  May 1, 14:00 - 16:00

  B1 L2 R2202

  gamma-convergence PDE

  We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 2: (1) Integral functionals with p growth, 1 p 1; The localisation method of Gamma-convergence; Integral representation.
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  On Knowledge Graph and Causality for Recommender Systems

  Prof. Guandong Xu, School of Software and Advanced Analytics Institute, University of Technology Sydney

  Apr 30, 14:00 - 15:00

  B1 L4 R4214

  text and data analytics data science data mining University of Technology Sydney

  Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.
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  Part 1: Introduction to homogenisation and Gamma-Convergence

  Filippo Cagnetti, Senior Lecturer in Mathematics, University of Sussex, UK

  Apr 30, 14:00 - 16:00

  B2 B3 A0215

  gamma-convergence PDE

  We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 1: (1) The homogenisation problem; (2) Gamma-convergence - definition, main properties, examples, comparison with pointwise limit; (3) Lower semicontinuity, lower semicontinuous envelope; (4) Coercivity
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  OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding

  Dr Philipp Jovanovic, École Polytechnique Fédérale de Lausanne (EPFL)

  Apr 29, 12:00 - 13:00

  B9 L2 H1

  scale out security transactions

  Designing a secure permissionless distributed ledger that performs on par with centralized payment processors such as Visa is challenging. Most existing distributed ledgers are unable to "scale-out" -- growing total processing capacity with number of participants -- and those that do compromise security or decentralization. This work presents OmniLedger, the first scale-out distributed ledger that can preserve long-term security under permissionless operation. OmniLedger ensures strong correctness and security by using a bias-resistant public randomness protocol to choose large statistically representative shards to process transactions, and by introducing an efficient cross-shard commit protocol to handle transactions affecting multiple shards atomically.
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  The route to chaos in routing games: Population increase drives period-doubling instability and chaos with Price of Anarchy equal to one

  Georgios Piliouras, Assistant Professor, Singapore University of Technology and Design (SUTD)

  Apr 29, 11:00 - 12:00

  B1 L3 R3119

  SUTD game-theoretic analysis robotics

  We study a simple learning dynamic model of routing (congestion) games to explore the effects of increasing the total demand on system performance. We focus on the most benign setting, non-atomic routing games with two parallel edges of linear cost, where all agents evolve using Multiplicative Weights Updates with a fixed learning rate.
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  1st Joint KAUST-MBSC Workshop

  Apr 28, 13:30 - 17:00

  B3 L5 R5220

  The first joint workshop between KAUST and Prince Mohammad Bin Salman College (MBSC). Presenters from both institutions will introduce their research projects in areas such as Game Theory, Operations Research, Finance, and Business & IT Operations. To download the Workshop's Program and Abstract Book click here.
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  InGaN-based light-emitting devices

  Daisuke Iida, Senior Research Scientist, Electrical and Computer Engineering

  Apr 28, 12:00 - 13:00

  B9 L2 H1

  MOVPE optoelectronics plasmonics photocatalysis solar energy

  III-Nitride-based light-emitting diodes (LEDs) are already commercialized for instance blue and green LEDs. It is very contributed as energy saving light source all over the world. This material is a very attractive material, capable of the emitting range is not only blue light but also UV and visible light. Moreover, application as a power device is also possible, it is one of the materials considered to lead the energy saving society in the future. Visible light LED has a wide range of applications. Visible light LED has a wide range of applications. we expect that there is some application, for instance, μ-LED display, optical communication, plant cultivation, medical treatment, and so on. In the seminar, I will talk about III-Nitride-based visible light emitting devices and introduce recent research with outstanding metalorganic chemical vapor deposition (MOCVD) growth technique.
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  Model Predictive Control Systems for Industrial Applications

  Fahad Albalawi , Research Scientist, Electrical and Computer Engineering

  Apr 28, 09:30 - 10:30

  B3 L5 R5209

  Controller design Model Predictive Control Signal processing

  Model Predictive Control (MPC) is an d advanced control strategy widely used in the process industries and beyond. Therefore, industry is interested in the developments of MPC formulations that can enhance safety, reliability, and economic profitability of chemical processes. Motivated by these considerations, the first part of this talk focuses on the development of methods for integrating process operational safety and process economics within model predictive control system designs.

Apr 21 - Apr 27, 2019

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  Almost everything you always wanted to know about NONLINEAR STATE SPACE MODELS (but were afraid to ask)

  Prof, David Stoffer, University of Pittsburgh, Pennsylvania, USA

  Apr 26, 15:00 - 18:00

  B1 L4 R4102

  university of pittsburgh linear guassian model Numerical Optimization

  Ever wonder why, when you fly to Jeddah you don't end up in Riyadh? The tracking devices use a nonlinear state space model to make sure your plane is on course. While inference for the linear Gaussian model is fairly simple, inference for nonlinear models can be difficult and often relies on derivative free numerical optimization techniques. A promising method that I will discuss is based on particle approximations of the conditional distribution of the hidden process given the data. This distribution is needed for both classical inference (e.g., Monte Carlo EM type algorithms) and Bayesian inference (e.g., Gibbs sampler).
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  Forecasting Multiple Time Series with One-Sided Dynamic Principal Components

  Prof. Daniel Peña Sánchez de Rivera, Department of Statistics, Universidad Carlos III de Madrid

  Apr 25, 16:00 - 17:00

  B1 L4 R4102

  Time Series big data

  Generalized Dynamic principal components are presented and it is shown how to define one side inear combinations of the present and past values of the series that minimize the reconstruction mean squared error (ODPC). It is shown that the ODPC introduced in this paper can be successfully used for forecasting high-dimensional multiple time series.
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  Advanced MTJ Sensory Devices for Industrial and Healthcare Applications

  Yousof A. Mashraei, Ph.D., Electrical and Computer Engineering

  Apr 25, 15:00 - 16:00

  B3 L5 R5209

  magnetic sensors IoT consumer electronics

  Magnetic sensors are deployed in many applications such as automotive, consumer electronics, navigation and data storage devices. Their market’s growth is driven by demands of higher performance and more integration; primarily to assist in the advancement of Internet of Things (IoT) and smart systems.
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  GaN-based power devices and power module

  Dr. Liu Xinke, Assistant Professor, Shenzhen University

  Apr 25, 13:00 - 14:00

  B2 L5 R5220

  Shenzhen University power devices power module

  Gallium nitride (GaN)-based power device, e.g. Schottky barrier diodes (SBDs) and high electron mobility transistors (HEMTs), have attracted considerable research interest and well recognized as the next generation high power and high temperature devices, owing to their ultralow conduction loss and fast switching under high voltage and high frequency operations.
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  Applications of random matrix theory for wireless communication and big data processing

  Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering

  Apr 25, 12:00 - 13:00

  B9 L2 H1

  Since the pioneer works of Telatar, random matrix theory has found a variety of applications in engineering disciplines that, to name a few, include wireless communication and signal processing. Its scope is now going far beyond the field of mathematics, being recognized as an indispensable tool for advanced research in engineering disciplines as can be evidenced by the dramatic increase in related publications. Recently, random matrix theory has found its way into the field of big data processing, allowing accurate characterization of the performance of many algorithms met in the field of machine learning.
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  Day 2 - Deep learning and the latest algorithms in artificial intelligence

  Prof. Xavier Bresson, Data Science and AI Research Centre at Nanyang Technological University (NTU) Singapore

  Apr 25, 09:00 - 17:00

  B4 B5 A0215

  Nanyang Technological University Deep learning machine learning

  The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.
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  Introduction to High Performance Computational Chemistry with an Application of Hierarchical Matrices

  Associate Professor Edmond Chow, School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology

  Apr 24, 14:00 - 15:30

  B2 B3 A0215

  multipole quantum chemistry Georgia Tech

  This talk begins with an introduction to quantum chemistry for HPC researchers. We discuss the main computational kernels, how their performance is limited by various bottlenecks, and algorithms and implementations that improve performance. One particular kernel is the calculation of the Coulomb matrix. Whereas the fast multipole method (FMM) can be used to rapidly compute the Coulomb potential for sets of point charges, continuous variants of FMM were developed in the 1990s for sets of charge distributions that arise in quantum chemistry.
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  Part 3: Variational structures and Fourier approximation techniques for mean-field game systems

  Dr. Levon Nurbekyan, Visiting Scholar, Université de Montréal

  Apr 24, 14:00 - 16:00

  B1 L3 R3119

  Université de Montréal mean-field games Partial Differential Equations

  Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems.
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  Day 1 - Deep learning and the latest algorithms in artificial intelligence

  Prof. Xavier Bresson, Data Science and AI Research Centre at Nanyang Technological University (NTU) Singapore

  Apr 24, 09:00 - 17:00

  B4 B5 A0215

  Nanyang Technological University Deep learning machine learning

  The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.
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  Inference for Spatial Trends

  Professor Ngai Hang Chan, Professor of Statistics, Chinese University of Hong Kong

  Apr 23, 16:00 - 17:00

  B1 L4 R4102

  Non-stationary spatial models are widely applicable in diverse disciplines, ranging from bio-medical sciences to geophysical studies. In many of theses applications, testing for structural changes in the trend and testing the specific form of the trend are highly relevant. A novel statistics based on a discrepancy measure over small regions is proposed in this paper. Such a measure can be used to construct tests for structural trends and to identify change boundaries of the trends. By virtue of the m-dependence approximation of a stationary random eld, asymptotic properties and limit distributions of these tests are established. The method is illustrated by simulations and data analysis.
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  Unstructured Computations on Emerging Architectures

  Mohammed AlFarhan

  Apr 23, 13:00 - 14:00

  B3 L5 R5209

  This dissertation describes detailed performance engineering and optimization of an unstructured computational aerodynamics software system with irregular memory accesses on a wide variety of multi- and many-core emerging high-performance computing scalable architectures, which are expected to be the building blocks of energy-austere exascale systems, and on which algorithmic- and architecture-oriented optimizations are essential for achieving worthy performance.
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  Convolutional Neural Networks on Graphs

  Prof. Xavier Bresson, NTU, Singapore

  Apr 23, 12:00 - 13:00

  B9 H2

  Nanyang Technological University Deep learning machine learning

  In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data.
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  Part 2: Variational structures and Fourier approximation techniques for mean-field game systems

  Dr. Levon Nurbekyan, Visiting Scholar, Université de Montréal

  Apr 22, 14:00 - 16:00

  B1 L3 R3119

  Université de Montréal mean-field games Partial Differential Equations

  Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 2: I will present new Fourier approximation techniques for nonlocal MFG systems and discuss how these techniques fit the variational framework presented on Day 1. Then I will introduce the Chambolle-Pock primal-dual hybrid gradient optimization method and apply a variant of this method to approximate nonlocal MFG models via Fourier approximation techniques above.
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  Design and Optimisation of Next generation Compact Photonic Sensors

  Prof. B. M. Azizur Rahman, City, University of London

  Apr 22, 12:00 - 13:00

  B3 L5 R5209

  sensors

  Although optical sensors incorporating grating inscribed and etched fibres are now sufficiently mature and well established in the market, however, designs based on more exotic nanowires and photonic crystal fibres are becoming increasingly important and showing much improved sensitivity by accessing a larger evanescent field. Similarly, novel planar design concepts, such as the silicon slot guide-based design is showing even greater promise, allowing the exploitation of well-developed CMOS fabrication technologies for potentially low-cost sensor elements.
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  Big Data Approaches to Scientific Output Prediction

  Prof. Yannis Manolopoulos, Open Univ. of Cyprus

  Apr 22, 12:00 - 13:00

  B9 L2 H1

  During the past two decades the availability of big scholarly data repositories such as Google Scholar, Elsevier Scopus offered tremendous opportunities to analyze scientific production and help develop models for it. The development of computerized analysis methods for these voluminous scholarly data allows to understand, quantify and predict research activities and the corresponding outcomes. The focus of this talk is on techniques to forecast future impact of a scientist; this is a very interesting problem because it allows for making effective hiring/promotion decisions and research fund allocation, among others.
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  Paving the Way for Next Generation Wireless Data Center Networks

  Amer AlGhadhban, Ph.D. Student, Electrical and Computer Engineering

  Apr 21, 13:00 - 14:00

  B1 L4 R4214

  big data artificial intelligence cloud services

  As big data, articial intelligence, cloud services, cellular infrastructure, content delivery; all of which entail interconnected and sophisticated computing and storage resources. Recent studies on traditional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack trac, and unidentied ow multi-classes best known as delay sensitive mice ow (MF) and throughput-hungry elephant ow (EF).