Events

May 12 - May 18, 2019

• 

  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
• 

  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.
• 

  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.
• 

  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

• 

  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.
• 

  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.
• 

  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

• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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

• 

  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).
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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).
• 

  Brief Introduction to the Fundamentals of Electrical Discharges in Liquids and Liquid/Solid Interfaces

  EMC Specialist, ABB AB – FACTS in Västerås, Sweden

  Apr 21, 12:00 - 13:00

  B9 L2 H1

  electrical discharges high-powered transformers

  In this seminar I will introduce the basics of the electrical discharges (plasma) in non-polar liquids and liquid/solid interfaces and their importance in specific applications such as high-powered transformers, capacitors, cables etc. The precursor mechanism of breakdown in these liquid and liquid/solid insulating systems are usually call streamers and their characteristics vary significantly with the nature of the liquid, voltage, pressure, electrode configuration, etc.
• 

  Singularity formation of Nonlinear Evolution Equations

  Dr. Van Tien Nguyen, NYU Abu Dhabi

  Apr 21, 12:00 - 13:00

  B3 L5 R5220

  Many central problems in geometry, mathematical physics and biology reduce to questions regarding the behavior of solutions of nonlinear evolution equations. The global dynamical behavior of bounded solutions for large times is of significant interest. However, in many real situations, solutions develop singularities in finite time. The singularities have to be analyzed in detail before attempting to extend solutions beyond their singularities or to understand their geometry in conjunction with globally bounded solutions. In this context we have been particularly interested in qualitative descriptions of blowup.

Apr 14 - Apr 20, 2019

• 

  On the Application of Extreme Value Theory to the Performance of Modern Wireless Communication Systems

  Assistant Professor Yazan H. Al-Badarneh, Electrical Engineering, University of Jordan

  Apr 18, 14:00 - 15:00

  B1 L3 R3119

  The University of Jordan

  Extreme value theory (EVT) deals with the asymptotic distributions of the extremes (maximum or minimum) of a set of N random variables, as N grows large. EVT is a powerful tool to analyze the performance of generalized user selection in modern wireless Communication systems. In this talk, we will provide an overview of EVT and its application to the performance of generalized user selection for multiuser traditional wireless and cognitive radio networks.
• 

  Estimation Problems for Partial and Fractional Differential Equations - Spring 2019

  Apr 18, 12:00 - 13:00

  B9 L2 H1

  Fractional differential equation Partial Differential Equations

  We will present some new methods for source and parameters estimation for partial and fractional differential equations and illustrate the results with some simulations and real applications.
• 

  Part 1: Variational structures and Fourier approximation techniques for mean-field game systems

  Dr. Levon Nurbekyan, Visiting Scholar, Université de Montréal

  Apr 17, 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 1: I will show how to transform suitable mean-field game (MFG) systems into infinite-dimensional convex optimization problems. Furthermore, I will present Uzawa’s algorithm and augmented Lagrangian approach for solving convex optimization problems. Finally, I will demonstrate how to apply these methods to approximate solutions of corresponding MFG systems.
• 

  On Strong Stability of Runge-Kutta Methods

  Dr. Hendrik Ranocha, Technische Universitat Braunschweig

  Apr 16, 14:00 - 15:00

  B1 L4 R4214

  TU Braunschweig pdes and odes Hyperbolic PDEs

  Runge-Kutta methods are classical and widespread techniques in the numerical solution of ordinary differential equations (ODEs). Considering partial differential equations, spatial semidiscretisations can be used to obtain systems of ODEs that are solved subsequently, resulting in fully discrete schemes. However, certain stability investigations of high-order methods for hyperbolic conservation laws are often conducted only for the semidiscrete versions.
• 

  Visualizing Trees

  Prof. Xiaoru Yuan, Peking University

  Apr 15, 12:00 - 13:00

  B9 L2 H1 R2322

  Peking University tree visualisation GoTree visual analytics map metaphor Social media

  In this talk, I will introduce a few recent works on tree visualization. First I will present a visualization technique for comparing topological structures and node attribute values of multiple trees. I will further introduce GoTree, a declarative grammar supporting the creation of a wide range of tree visualizations. In the application side, visualization and visual analytics on social media will be introduced. The data from social media can be considered as graphs or trees with complex attributes. A few approaches using map metaphor for social media data visualization will be discussed.
• 

  A New DC-AC Interconnected Modular Multilevel Converter for Multiple Applications

  Prof. Ahmed Allehyani, University of Jeddah

  Apr 14, 12:00 - 13:00

  B9 L2 H1

  university of jeddah high-power-density IMMC

  This seminar highlights a new high-power density Interconnected Modular Multilevel Converter (IMMC) with sinusoidal output voltage for multiple applications. The proposed converter utilizes wide band gap devices at a high switching frequency to achieve compact size/weight/volume. The proposed converter is modular in construction, employs high frequency L-C components and can be stacked for voltage sharing.

Apr 7 - Apr 13, 2019

• 

  Wearable antennas, 3D-Printed RF Materials and Metamaterials Research

  Dr. Will Whittow, Loughborough University, UK

  Apr 11, 14:00 - 15:00

  B1 L4 R4214

  This talk will introduce Loughborough University and show some of the Engineering research and facilities. The talk will then focus on the research of Dr. Whittow and his colleagues at Loughborough University. This will include wearable antennas, inkjet printing, RFID tags, 3d-printing and metamaterials. The work will include some results from SYMETA which is a $7 million project to develop RF-devices.
• 

  The Physical Review Applied : An Insider’s Look

  Prof. Badreddine M. Assouar, Institut Jean Lamour, University of Lorraine

  Apr 11, 12:00 - 14:00

  B1 L2 R2202

  manuscript peer-review

  The idea behind this presentation is to give to the potential contributors/authors a general view about the Physical Review Family of Journals, and more specifically about Physical Review Applied journal.
• 

  Space-time conservation element and solution element (CESE) method for computational fluid dynamics

  Hua Shen

  Apr 11, 12:00 - 13:00

  B9 L2 H1

  computational fluid dynamics ​CFD

  Space-time conservation element and solution element (CESE) method is a unique finite-volume-type method for computational fluid dynamics (CFD). This approach has several attractive properties, including: (i) unified treatment of the space and time such that only one step is required to construct high-order schemes; (ii) a highly compact stencil regardless of the order of the accuracy; (iii) easiness of extension to any arbitrary shape of polygonal elements. Since its inception, the CESE method has achieved great success in different areas.
• 

  Mini Course Part 3-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

  Prof. Jan Giesselmann, Technical University of Darmstadt, Germany

  Apr 9, 16:00 - 17:00

  B1 L3 R3119

  Multi-face flows numerical methods

  In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation
• 

  Acoustic metamaterials and metasurfaces for controllable wave manipulation

  Prof. Badreddine M. Assouar, Institut Jean Lamour, University of Lorraine

  Apr 9, 15:00 - 16:00

  B1 L2 R2202

  acoustic-elastic metamaterials phononics Wave Physics Metasurfaces Institut Jean Lamour University of Lorraine

  I will provide an overview on recent researches on acoustic and elastic metamaterials and metasurfaces for controllable wave manipulation, we are developing in my group in the University of Lorraine. I first will present some advances related to low-frequency acoustic and vibration shielding/absorption making use of metamaterials1,2 and metasurfaces3,4, and describe the added value that such artificial engineered materials can bring to consider some innovative applications.
• 

  From Bioinformatics to Translational and Health Informatics: Whitening the Black Box

  Prof. Yasser EL-Manzalawy, Information Sciences and Technology, Pennsylvania State University

  Apr 8, 12:00 - 13:00

  B9 H1

  Pennsylvania State University machine learning

  Machine learning has been extensively used in developing predictive models for a variety of bioinformatics tools. In many of these applications, to achieve the highest possible predictive performance, black box models (e.g., SVMs and Neural Networks) had been preferred over white box models (e.g., Decision Trees and Rule-based models). First, I argue that sacrificing interpretability for the sake of performance is a reasonable decision for many bioinformatics applications. However, I will demonstrate that uncareful utilization of black box models can lead to misinterpretations of the results.
• 

  Mini Course Part 2-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

  Prof. Jan Giesselmann, Technical University of Darmstadt, Germany

  Apr 7, 16:00 - 17:00

  B1 L3 R3119

  Multi-face flows numerical methods

  In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation
• 

  Multiphysics Simulation of Semiconductor-based Terahertz Devices

  Liang Chen, Research Scientist, Electrical and Computer Engineering

  Apr 7, 12:00 - 13:00

  B9 H1

  computational physics electromagnetics electronic transport nanostructures photoconductive antennas

  Semiconductor-based terahertz (THz) devices such as photoconductive antennas (PCAs) and photomixers (PMs) are widely studied as promising candidates of THz source generation and signal detection. Recent experimental research has shown that using nanostructures in the design of these devices dramatically enhances their optical-to-THz conversion efficiency, possibly allowing their use in widespread industrial applications. However, the nanostructures also increase the complexity of design and fabrication.

Mar 31 - Apr 6, 2019

• 

  The Discrete Charm of Geometry

  Prof. Alexander I. Bobenko, Technische Universität Berlin

  Apr 3, 13:15 - 14:45

  B9 L2 H2

  mathematics discrete models geometric structures continuous shapes

  How is modern mathematical teamwork carried out? The multiple award-winning film The Discrete Charm of Geometry by Ekaterina Eremenko will screen on April 3rd after the CEMSE Dean's Distinguished Lecture Discrete conformal mappings and Riemann Surfaces: Theory and Applications by Prof. Alexander I. Bobenko, Technische Universität Berlin. Following the screening, Prof. Bobenko will be available for a Q&A session.