Events

Jan 8 - Jan 14, 2023

• 

  Nonlocal Aggregation-Diffusion Equations: entropies, gradient flows, phase transitions and applications

  Prof. Jose Carrillo, Department of Mathematics, University of Oxford, UK

  Jan 10, 15:30 - 17:00

  B2 L5 R5209

  Fokker-Planck equations Cucker-Smale orientation dynamics McKean-Vlasov equations Keller-Segel-type models

  This talk will be devoted to an overview of recent results in understanding the bifurcation analysis of nonlinear Fokker-Planck equations arising in a myriad of applications such as consensus formation, optimization, granular media, swarming behavior, opinion dynamics, and financial mathematics to name a few. We will present several results related to localized Cucker-Smale orientation dynamics, McKean-Vlasov equations, and nonlinear diffusion Keller-Segel-type models in several settings. We will show the existence of continuous or discontinuous phase transitions on the torus under suitable assumptions on the Fourier modes of the interaction potential.
• 

  Challenges and Recent Progress on AlN Growth by the PVT Method

  Prof.Liang Wu, Shanghai University and CEO of Ultratrend Technologies Inc.

  Jan 8, 15:00 - 16:00

  B3 L3 R3334

  AlN crystals

  In this talk, an overview of the general strategies and associated sublimation reactors to grow AlN crystals by PVT method will be given. Particular interests are focused on different AlN growth strategies for large-size AlN crystal growth, and the advantages and disadvantages of these growth strategies will also be addressed in great detail.

Jan 1 - Jan 7, 2023

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  Bayesian Non-parametric Models for Time Series Decomposition

  Guillermo C. Granados Garcia, Ph.D. Student, Statistics

  Jan 5, 17:00 - 19:00

  B1 R4214

  spectral density function

  The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition, which are data-driven approaches that identify (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks).

Dec 11 - Dec 17, 2022

• 

  Monodromy and approach to equilibrium for viscoelastic models allowing microstructure

  Prof. Sir John Ball, Department of Mathematics, Heriot-Watt University, Edinburgh, UK

  Dec 13, 15:30 - 17:00

  B3 L5 R5220

  For certain models of one-dimensional viscoelasticity, there are infinitely many equilibria representing phase mixtures. In order to prove convergence as time tends to infinity of solutions to a single equilibrium, it seems necessary to impose a nondegeneracy condition on the constitutive equation for the stress. The talk will explain this and show how in some cases, the nondegeneracy condition can be proved using the monodromy group of a holomorphic map. This is joint work with Inna Capdeboscq and Yasemin Şengül. John Ball is Professor of Mathematics at Heriot-Watt University, Edinburgh, and formerly Sedleian Professor of Natural Philosophy at Oxford. He is the current President of the Royal Society of Edinburgh, and a former President of the International Mathematical Union. He specializes in the applications of nonlinear analysis to problems of materials science, liquid crystals and computer vision. Among various awards he received the 2018 King Faisal Prize for Science, the 2018 Leonardo da Vinci Award of the European Academy of Sciences and the 2022 De Morgan Medal of the London Mathematical Society.
• 

  Performance Analysis of Free Space Optical (FSO) Link with Pointing Errors

  KugJin Jung, Ph.D., Electrical and Computer Engineering

  Dec 13, 10:00 - 12:00

  B1 L3 R3119

  Free Space Optics

  Free-space optical communication (FSO) has been proposed as an attractive alternative to radio frequency communication in the sense that it provides wide bandwidth and high capacity without the requirement of a license. However, the scalability of the FSO link is limited by pointing errors, atmospheric turbulence, and loss. Especially, when it comes to the FSO link between moving platforms, it is imperative works to analyze the statistical channel model considering accurate pointing errors and atmospheric turbulence at the same time. In this paper, we analyze the performance of FSO links over various atmospheric situations with pointing errors.
• 

  Liquid crystals from a mathematical perspective

  Prof. Sir John Ball, Department of Mathematics, Heriot-Watt University, Edinburgh, UK

  Dec 12, 12:00 - 13:00

  B1 L4 R4102

  Liquid crystals are materials whose properties are intermediate between normal fluids and solid crystals, and have widespread use as the working substance for computers, TV, and watch displays. The lecture will introduce these materials and what mathematics can say about them, and in particular, discuss how different theories of liquid crystals describe orientational defects in different ways.
• 

  A tractable model for coverage analysis of satellite downlink networks

  Namyoon Lee, Wireless Communications, Korea University

  Dec 11, 16:00 - 17:00

  B1 L3 R3119

  Satellite downlink networks

  Satellite networks are promising to provide ubiquitous and high-capacity global wireless connectivity.
• 

  The Wild West of NLP Modeling, Evaluation, and Documentation

  Dr. Nazneen Rajani, Research Lead, Hugging Face, California

  Dec 11, 14:00 - 15:00

  B2 L5 R5220

  artificial intelligence Natural Language Processing machine learning

  Are commercial APIs from Microsoft, Google, and Amazon for NLP tasks any better than rule-based systems? Is documentation for LLMs accessible to non-expert users in the industry? Our work on creating a unified toolkit for evaluation (robustness gym) and reporting (interactive model cards) attempts to address these questions.
• 

  Performance Analysis and Throughput Maximization of Satellite Communication Systems with Randomly Located Users

  Dong Hyoun Na, Ph.D., Electrical and Computer Engineering

  Dec 11, 08:00 - 10:00

  B1 L3 R3119

  satellite communication

  Satellite communication (SatCom) is an essential component of next-generation wireless communications. The existing terrestrial network will be overwhelmed due to the rapid growth of demand for data and serving remote areas by using only terrestrial networks is demanding. In addition, terrestrial communications are susceptible to natural disasters such as earthquakes and floods. In order to overcome these disadvantages of the terrestrial communication systems, SatCom systems are being deployed and covering remote or sparsely populated areas. However, research on SatCom is still not enough and it has not been studied as much as on terrestrial communication.

Dec 4 - Dec 10, 2022

• 

  On the role of pair correlations in the evolution of Bose-Einstein condensates

  Prof. Manoussos Grillakis, Department of Mathematics, University of Maryland in College Park.

  Dec 7, 15:30 - 17:00

  B1 L3 R3119

  nonlinear Schr¨odinger equation

  A Bose gas at zero temperature is described by a mean field which satisfies the cubic nonlinear Schr¨odinger equation (NLS) otherwise known as the Gross- Pitaevski equation. The mean field describes the evolution of the condensate in an average sense. I will describe a technique that introduces pair correlations in the evolution of the condensate. The resulting approximation tracks the evolu- tion of the condensate in norm provided that the pair wave-function satisfies an interesting system of coupled NLS equations. I will discuss the nonlinear struc- ture of the NLS system as well as a novel approach to the question of global existence of solutions of the system.
• 

  Optimal-complexity Multigrid Solvers for High-order FEM in the de Rham Complex

  Prof. Patrick Farrell, University of Oxford

  Dec 6, 15:30 - 17:00

  B1 L3 R3119

  In this work we present multigrid solvers for high-order finite element discretizations of these Riesz maps with optimal complexity in polynomial degree, i.e. With the same time and space complexity as sum-factorized operator application.
• 

  Modelling and Simulation of Biological Systems - 2022-12-06

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Dec 6, 12:00 - 13:00

  B9 L2 R2322

  Biological systems

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.
• 

  Reynolds-robust preconditioners for the stationary incompressible Navier--Stokes equations

  Prof.Patrick Farrell, University of Oxford

  Dec 5, 12:00 - 13:00

  B9 L2 R2322 H1

  Navier-Stokes equation Robustness

  Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.
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  Characterization of Natural Bacterial Pathogens with Genomic and Predictive Modelling

  Dr. Danesh Moradigaravand, Infectious Disease Epidemiology lab, BESE, KAUST

  Dec 5, 12:00 - 13:00

  B3 L5 R5209

  machine learning Metagenomics

  In this talk, I will first present how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of multidrug resistant bacterial pathogens provided an in-depth understanding of the epidemiology and evolution of these strains on epidemiological time scales. I will then discuss the characterization of the genomic repertoire of bacterial traits using a combination of machine learning, whole genome sequencing and large-scale phenotypic assays. I will then present the leverage of predictive modelling to predict bacterial features, e.g. antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers. I will finally discuss how large-scale phenotypic assays enabled us to identity genes underlying morphogenesis and biofilm formation.
• 

  High Efficiency Photovoltaic Cells and their Applications to Green Hydrogen Production

  Prof. Yoshiaki Nakano, Electrical Engineering and Information Systems, The University of Tokyo

  Dec 4, 14:30 - 15:30

  B4 L5 R5209

  III - V compound semiconductors photovoltaics

  This lecture will review research trends of III-V compound semiconductor ultra-high efficiency photovoltaic (PV) cells based on multiple junctions and quantum well/dot approaches. It will also introduce field trials of highly efficient green hydrogen production through the direct connection of electrolyzers and concentrator PV modules with the above-mentioned compound semiconductor solar cells built-in.
• 

  The Internet of Bodies: A Gateway to Holistic Healthcare - 2022-12-04

  Ahmed Eltawil, Associate Dean for Research, Computer, Electrical and Mathematical Sciences and Engineering

  Dec 4, 12:00 - 13:00

  B9 L2 R2322 H1

  The Internet of Bodies

  The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where wearable, ingestible, injectable, and implantable smart objects form a network in, on, and around the human body.

Nov 27 - Dec 3, 2022

• 

  Generic Regularity in Obstacle Problems

  Professor Alessio Figalli, ETH Zurich

  Nov 29, 16:00 - 17:00

  KAUST

  The classical obstacle problem consists of finding the equilibrium position of an elastic membrane whose boundary is held fixed and which is constrained to lie above a given obstacle. By classical results of Caffarelli, the free boundary is smooth outside a set of singular points. Explicit examples show that the singular set could be, in general, as large as the regular set. In a recent paper with Ros-Oton and Serra we show that, generically, the singular set has codimension 3 inside the free boundary, solving a conjecture of Schaeffer in dimension n ≤ 4. The aim of this talk is to give an overview of these results.
• 

  A unifying partially-interpretable framework for neural network-based extreme quantile regression

  Raphaël Huser, Associate Professor, Statistics

  Nov 29, 12:00 - 13:00

  B9 L2 R2322

  Artificial Neural Network quantile regression

  In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neural networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.
• 

  Modelling and Simulation of Biological Systems - 2022-11-28

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Nov 28, 12:00 - 13:00

  B2 L5 R5209

  applied mathematics bioengineering

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.
• 

  Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

  Dr.Syed Adnan Yusuf

  Nov 28, 12:00 - 13:00

  B9 L2 R2322 H1

  Computer Vision robotics automation IoT nlp artificial intelligence

  This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.
• 

  On the collapse mechanism of Wave Maps

  Prof. Manoussos Grillakis, Departments of Mathematics, University of Maryland

  Nov 27, 13:00 - 15:00

  B1 L4 R4214

  Partial Differential Equations

  The Wave Map system describes the evolution of waves constrained on a (Riemannian) manifold. For the 2 + 1 dimensional problem, when the target manifold is a sphere, the solution collapses in finite time. The Analysis is due to the pioneering work of Merle, Paphael and Rodnianski. Motivated by their work I will present a somewhat novel approach of the collapsing mechanism which is based on a view of the equations as a nonlinear gauge system. This is joint work with Dan Geba.
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  A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Graduate Seminar

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Nov 27, 12:00 - 13:00

  B9 L2 R2322 H1

  Applied complexity

  Complexity studies physical processes that are generally unpredictable or difficult to predict and depend on many degrees of freedom. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.

Nov 20 - Nov 26, 2022

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  Introduction to Quantum Computing

  Arnaud Carignan Dugas, Research Scientist, Quantum Computing University of Waterloo

  Nov 23, 14:00 - 16:00

  Online Event

  quantum computing

  In this presentation, we will review past, present, and future eras of quantum computing - such as Near Intermediate Scale Quantum (NISQ) and Utility Scale Quantum (USQ) eras – and cover their respective overarching aspirations and limitations. Once properly situated, we can identify the different roles that academic and industrial players are taking to drive this emerging technology and circumvent current systems' limitations.
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  Machine learning in hardware via trained metasurface encoders: theory, design and applications

  Maksim Makarenko, Ph.D. Student, Electrical and Computer Engineering

  Nov 23, 10:00 - 12:30

  B2 L5 R5209

  machine learning artificial intelligence photonics

  In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).
• 

  Sharp Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces

  Assistant Professor Jonathan Siegel, Texas A and M University

  Nov 22, 15:30 - 17:00

  B1 L3 R3119

  ReLU Neural Network Sobolev Spaces

  Sobolev spaces are centrally important objects in PDE theory. Consequently, to understand how deep neural networks can be used to numerically solve PDEs a necessary first step is to determine now efficiently they can approximate Sobolev functions. In this talk we consider this problem for deep ReLU neural networks, which are the most important class of neural networks in practical applications.
• 

  Lipschitz learning and the infinity-Laplacian

  Miguel Urbano, Professor, Applied Mathematics and Computational Science

  Nov 22, 12:00 - 13:00

  B9 L2 R2322

  Lipschitz additive nonlinear uncertainty infinity Laplacian

  Infinity-harmonic functions have recently found application in Semi-Supervised Learning, in the context of the so-called Lipschitz Learning. With this application in mind, we will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions and its several drawbacks, leading to the notion of AMLE (Absolutely Minimising Lipschitz Extension).
• 

  On training algorithms for neural networks

  Jinchao Xu, Professor, Applied Mathematics and Computational Science

  Nov 21, 12:00 - 13:00

  B9 L2 R2322 H1

  Deep learning deep neural networks

  In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms
• 

  Principles and Algorithms for Multi-Agent Decision Making

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Nov 20, 12:00 - 13:00

  B9 L2 R2322 H1

  Multi-Agent Decision Making

  A multi-agent system consists of individual agents sharing information and coordinating for collective decision making. The study of multi-agent decision making has important implications in conceiving networked engineering systems - a team of mobile robots or a fleet of drones - that can effectively coordinate to carry out assigned missions. Modeling such system as feedback interconnections of many smaller units allows us to examine its long-term behavior using analytical tools from feedback control theory, such as Lyapunov stability and bifurcation analysis. In this presentation, we discuss how such tools can be used to predict asymptotic behavior of the agents' decision making process and also to design computational models of the decision making process.

Nov 13 - Nov 19, 2022

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  Interval Prediction for Spatial Data Using Deep Learning Models

  Ghulam Qadir, Posdoctoral fellow, Computational Statistics group at Heidelberg Institute for Theoretical Studies, Germany

  Nov 17, 10:00 - 11:00

  B1 L4 R4102

  Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.
• 

  Functional Differential Equations in Models of Collective Behavior - Graduate Seminar

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Science

  Nov 15, 12:00 - 13:00

  B9 L2 R2322

  Functional Differential Equation

  The talk will give an overview of recent results for models of collective behavior governed by functional differential equations. It will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role.
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  Recent Progress in Aeronautical Sciences: Exquisite Stability of Insect Flight and a New Theory of Lift

  Prof. Haithem Taha, Associate Professor, Department of Mechanical and Aerospace Engineering at the University of California, Irvine

  Nov 14, 12:00 - 13:00

  B2 L5 R5220

  Aeronautical Sciences Insect Flight New Theory of Lift

  In this talk, I will present a special variational principle that we revived from the history of analytical mechanics: Hertz’ principle of least curvature. Using this principle, we developed a general (dynamical) closure condition that is, unlike the Kutta condition, derived from first principles. In contrast to the classical theory, the proposed variational theory is not confined to sharp-edged airfoils, i.e., it allows, for the first time, theoretical computation of lift over arbitrarily smooth shapes, thereby generalizing the century-old lift theory of Kutta and Zhukovsky. Moreover, the new variational condition reduces to the Kutta condition in the special case of a sharp-edged airfoil, which challenges the widely accepted concept regarding the viscous nature of the Kutta condition. We also generalized this variational principle to Navier-Stokes’, thereby discovering the fundamental quantity that Nature minimizes in every incompressible flow.
• 

  Parameter-Free Online Optimization: Past, Present, and Future

  Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

  Nov 14, 12:00 - 13:00

  B9 L2 R2322

  Parameter-free online optimization machine learning

  Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.
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  Multifunctional Electronic Devices: Towards Sustainable Electronics

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

  Nov 13, 12:00 - 13:00

  B9 L2 R2322 H1

  Multifunctional Electronic Devices

  In this talk, I will discuss multi-functional smart electronic devices which can perform the roles of multiple conventionally discrete components at once.

Nov 6 - Nov 12, 2022

• 

  Introduction to Computer Simulation - A short course II - Part 3

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 10, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Introduction to Computer Simulation - A short course II - Part 2

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 9, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Parallel multilevel solvers for virtual element discretizations of saddle point problems

  Prof. Simone Scacchi, Associate Professor of Numerical Analysis at the Department of Mathematics of the University of Milan

  Nov 8, 15:30 - 17:00

  B1 L3 R3119

  Virtual Element Method

  In this seminar, we will present our work on Virtual Element Method (VEM) approximations. The Virtual Element Method is a recent numerical technique for solving partial differential equations on computational grids constituted by polygonal or polyhedral elements of very general shape. This work aims to develop effective linear solvers for general order VEM approximations of three-dimensional scalar elliptic equations in mixed form and Stokes equations. To this end, we consider block algebraic multigrid preconditioners and balancing domain decomposition by constraints (BDDC) preconditioners. The latter allows us to use conjugate gradient iterations, albeit the algebraic linear systems arising from the discretization of the differential problems are indefinite, ill-conditioned, and of saddle point nature.
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  The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics

  Daniele Durante, Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy

  Nov 8, 15:00 - 16:00

  B1 L4 R4102

  bayesian inference

  In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.
• 

  Modeling water waves: from tsunamis to the kitchen sink

  David Ketcheson, Professor, Applied Mathematics and Computational Science

  Nov 8, 12:00 - 13:00

  B9 L2 R2322

  water waves Tsunamis Statistical Modelling

  Surface water waves are a physically important phenomenon with which we all have some experience. They are also surprisingly complex and interesting from a mathematical perspective. I will discuss two recent projects in water wave modeling. The first deals with ocean waves, such as tsunamis, passing over the continental slope. It has long been known that the amplification of such waves is greater than what the traditional transmission coefficient would predict.
• 

  Introduction to Computer Simulation - A short course II - Part 1

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 8, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Numerical approximations of Mean-Field-Games

  Serikbolsyn Duisembay, Ph.D. Student, Applied Mathematics and Computational Science

  Nov 7, 14:00 - 16:00

  B1 L2 R2202

  mean-field games

  In this thesis, we present three projects. First, we investigate the numerical approximation of Hamilton-Jacobi equations with the Caputo time-fractional derivative. We introduce an explicit in time discretization of the Caputo derivative and a finite-difference scheme for the approximation of the Hamiltonian. We show that the approximation scheme is stable under an appropriate condition on the discretization parameters and converges to the unique viscosity solution of the Hamilton-Jacobi equation.
• 

  Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

  Tobias Isenberg, Senior Research Scientist, Inria

  Nov 7, 12:00 - 13:00

  B9 L2 R2322 H1

  virtual reality data visualisation Scientific Visualization

  In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
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  Multivariate Functional Data Analysis and Visualization

  Zhuo Qu, Ph.D. Student, Statistics

  Nov 7, 11:00 - 13:00

  B3 L5 R5220

  functional data analysis multivariate functional data statistics

  As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.
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  Integrating Terrestrial and Non-terrestrial Networks: 3D Opportunities and Challenges

  Giovanni Geraci, Assistant Professor, Pompeu Fabra University, Barcelona

  Nov 6, 16:00 - 17:00

  B1 L3 R3119

  Integrating Terrestrial and Non-terrestrial Networks

  In a quest for anything, anytime, anywhere connectivity, next-generation wireless networks are envisioned to break the boundaries of the current ground-focused paradigm and fully embrace aerial and spaceborne communications.
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  Towards sustainable and resilient multi-energy infrastructure systems: water-energy nexus

  Mohannad Alhazmi, Electrical Engineering, King Saud University

  Nov 6, 12:00 - 13:00

  B9 L2 R2322 H1

  With the widespread deployment of advanced heterogeneous technologies and the sharply-growing complexity in our modern society, there is an increasing demand for risk-aware management and joint operation of interconnected infrastructures and lifeline networks.

Oct 30 - Nov 5, 2022

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  Flexible Electronics Beyond Mechanical Flexibility: Multifunctional Flexible Semiconductor Materials and Devices for Photonic, Electronic, Energy, and Sensing Applications

  Prof. Jae-Hyun Ryou, Mechanical Engineering, University of Houston

  Nov 3, 12:00 - 13:00

  B9 L3 R3223

  flexible photonic energy sensing devices

  Flexible electronics is an emerging and widely explored area. Most research groups in the area focus on fabrication processes to provide mechanical flexibility and their use in bendable and stretchable applications. Also, most semiconductors employed in flexible electronics are non-single-crystalline thin films which compromise the performance of the flexible devices.
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  Wide Bandgap Semiconductor Device Design via Machine Learning

  Rongyu Lin, Ph.D., Electrical and Computer Engineering

  Nov 2, 15:30 - 17:30

  B2 L5 R5220

  machine learning light gradient boosting machine extreme gradient boosting

  This dissertation presents novel approaches to the design of electrical and optical wide bandgap semiconductor devices, which opens new avenues for future research. It is possible that it might be used in a broad variety of sectors, including illumination, sensing, disinfection, and power devices by using TCAD and machine learning to deliver quick forecasts of the III-nitride semiconductor device.
• 

  Autonomy for recreational vessels: Safe intelligence at your finger tips Pablo Afman, ATL Robotics

  Juan-Pablo Afman

  Nov 2, 12:00 - 13:00

  B2 L5 R5220

  robotics intelligent systems

  Abstract Thanks to ever improving sensing, computing, and actuating technologies, autonomy is fast becoming a practical tool for many marine applications. Pushed by the general interest in autonomy and attracted by its underlying capabilities, this talk will present robotic technologies that enable safety and convenience for recreational boaters and marine professionals alike. Beginning with a Grady-White offshore fishing boat powered by two 425 HP engines, we present demonstrated functionalities such as collision avoidance, auto-docking, dynamic positioning, track and follow, and "take me
• 

  Numerical approximation of McKean-Vlasov SDEs via Stochastic Gradient Descent

  Prof. Gonçalo dos Reis, School of Mathematics, University of Edinburgh

  Nov 1, 15:30 - 17:00

  B1 L3 R3119

  stochastic gradient descent McKean-Vlasov SDEs

  We propose a novel approach of numerically approximate McKean-Vlasov SDEs that avoids the usual interacting particle approximation and Propagation of Chaos results altogether.
• 

  Multilevel Bayesian Neural Networks

  Nov 1, 12:00 - 13:00

  B9 L2 R2322

  Bayesian computational technique Applied Machine Learning

  Abstract In this talk we consider the application of multilevel Monte Carlo for Bayesian computation tasks in machine learning. There has recently been a synergy of statisticsand machine learning, promoting the application and development of new methodologies.Based on this we promote the use of multilevel Monte Carlo, which is a technique used toreduce the cost to attain a particular order of MSE with trace-class neural network priors. We provide some theoretical insights, and demonstrate the performance of our methodologyon different model problems such as classification and reinforcement
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  Microstructural Analysis and Engineering of III-Nitride-Based Heterostructures for Optoelectronic Devices

  Martín Velázquez Rizo, Ph.D. Student, Electrical and Computer Engineering

  Nov 1, 10:00 - 11:30

  B1 L3 R3119

  photocatalytic energy conversion

  This dissertation presents a variety of methodological approaches to characterize, from a microstructural point of view, different properties of novel III-nitride-based heterostructures and devices. The results of the various characterizations contributed to developing novel LEDs and photocatalysts. The analyses and results presented in this dissertation strongly relied on the analytical capabilities offered by transmission electron microscopy, which proved to be a convenient and versatile tool for the characterization of many aspects related to the fabrication of III-nitride-based optoelectronic devices.