Events

Jan 29 - Feb 4, 2023

• 

  Prototypical Random Walk Learning Mechanisms for Few-shot Learning, Novel Visual Generation, and (Continual)? Zero-shot Recognition

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Jan 30, 12:00 - 13:00

  B9 L2 H2 H2

  prototypical random walks zero-shot recognition

  In this talk, we will define prototypical random walks, a mechanism we introduced to improve visual classification with limited data (few-shot learning), and then developed the mechanism in a conceptually different way to facilitate novel image generation and unseen class recognition tasks. More specifically, in the few-shot learning setting, we will show how we can develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.
• 

  6G Summit on Connecting the Unconnected

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

  Jan 30, 08:30 - Feb 1, 13:00

  B19 L3 H1

  6G connectivity wireless connectivity

  The role of Internet and Communication Technology (ICT) in bringing about a revolution in almost all aspects of human life needs no introduction. As the standardization of the fifth generation (5G) of wireless communication systems (WCSs) has been completed, 6G is expected to be the next focus in wireless communication and networking and aim to provide new superior communication services to meet the future hyper-connectivity demands in the 2030s. With this background, this Summit aims to go over the recently proposed solutions not only to connect the unconnected/under-connected but also to super-connect the connected.
• 

  Machine Learning for Biomedical Engineering: from Oncological Electroporation to Gesture Recognition

  Luciano Tarricone, Department of Innovation Engineering, University of Salento

  Jan 29, 12:00 - 13:00

  B9 L2 H2

  machine learning

  In this presentation an overview is given on the most important research activities performed at the EML2.

Jan 22 - Jan 28, 2023

• 

  Securing data against non-conventional adversaries

  Prof. Mohammad Mannan, Concordia Institute for Information Systems Engineering, Concordia University, Montreal

  Jan 26, 15:30 - 16:30

  B4 L5 R5220.

  data security

  I will discuss four related proposals: Gracewipe (coercion-resistant disk data deletion), Hypnoguard (cold-boot protection for RAM data in sleep), SafeKeeper (protecting web credentials from rougue IT admins), and Blindfold (protecting PKI private keys from human admins). While our solutions are possibly a step forward, more importantly, we highlight pitfalls of such solutions against a strong adversary.
• 

  Towards Richer Video Representation for Action Understanding

  Humam Alwassel, Ph.D., Computer Science

  Jan 23, 18:30 - 20:30

  B2 L5 R5209

  Computer Vision machine learning Human Activity Recognition

  With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.
• 

  Principles and practice of TEE replication (and beyond)

  Prof.Rodrigo Rodrigues, Instituto Superior Tecnico (ULisboa)

  Jan 23, 12:00 - 13:00

  B9 L2 R2322 H1

  TEE replication TEEMS CFT replication protocol

  Trusted Execution Environments (TEEs) ensure the confidentiality and integrity of computations in hardware. Subject to the TEE's threat model, the hardware shields a computation from most externally induced fault behavior except crashes. As a result, a crash-fault tolerant (CFT) replication protocol should be sufficient when replicating trusted code inside TEEs. However, TEEs do not provide efficient and general means of ensuring the freshness of the external, persistent state. Therefore, CFT replication is insufficient for TEE computations with an external state, as this state could be rolled back to an earlier version when a TEE restarts. Furthermore, using BFT protocols in this setting is too conservative, because these protocols are designed to tolerate arbitrary behavior, not just rollback during a restart.
• 

  The Internet of Bodies: A Gateway to Holistic Healthcare - 2023-01-22

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

  Jan 22, 12:00 - 13:00

  B9 L2 R2322

  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.

Jan 15 - Jan 21, 2023

• 

  Aggregation-Diffusion and Kinetic PDEs for collective behavior: applications in the sciences

  Prof. José Antonio Carrillo de la Plata, Mathematical Institute, University of Oxford

  Jan 15, 16:00 - 17:00

  B1 L3 R3119

  Partial Differential Equations University of Oxford

  In this seminar, I will present a survey of micro, meso and macroscopic models where repulsion and attraction effects are included through pairwise potentials. I will discuss their interesting mathematical features and applications in mathematical biology and engineering. Qualitative properties of local minimizers of the interaction energies are crucial in order to understand these complex behaviors. I will showcase the breadth of possible applications with three different phenomena in applications: segregation, phase transitions, and consensus.

Jan 8 - Jan 14, 2023

• 

  Simulation and Calibration of Uncertain Space Fractional Diffusion Equations

  Hasnaa Alzahrani, Ph.D. Student, Applied Mathematics and Computational Science

  Jan 10, 16:00 - 18:00

  B1 L2 R2202

  This dissertation explores the uncertainty propagation in space fractional diffusion equations in one and multiple dimensions with variable diffusivity and order parameters.
• 

  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

• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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.
• 

  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

• 

  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.
• 

  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

• 

  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.
• 

  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.
• 

  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.
• 

  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.
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  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.
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  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.