Events

Jan 8 - Jan 15, 2023

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  Simulation and Calibration of Uncertain Space Fractional Diffusion Equations

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

  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.
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  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.

Jan 1 - Jan 15, 2023

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  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 8, 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 18, 2022

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  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.

Dec 4 - Dec 18, 2022

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  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.
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  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 11, 2022

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  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, Applied Mathematics and Computational Sciences

  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.
• 

  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.
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  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.

Nov 27 - Dec 11, 2022

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  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.
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  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 4, 2022

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  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.
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  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.
• 

  Opportunities and Challenges in Free Space Optics

  Prof. Ozdal Boyraz, University of California, Irvine, USA

  Nov 28, 14:15 - 15:00

  B2 5220

  nter-Satellite Communications PNT satellite communication FSO systems

  Opportunities and Challenges in Free Space Optics: Inter-Satellite Communications, Lidars, and PNT (Positioning, Navigation, and Timing). Abstract Recent developments in communications, such as providing broadband access to rural areas and enabling high bandwidth inter-satellite communications open new opportunities in free space optical communications. In this talk, recent testbed development efforts on more than 10km free space optical communication link development with more than 80Gb/s data capacity under the NSF PAWR program in rural Iowa State. Additionally, present challenges associated
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  Data Assimilation for Flood Real Time Estimation: Challenges and Methods

  Prof. Christian Claudel, The University of Texas at Austin

  Nov 28, 13:15 - 14:00

  B2 5220

  data assimilation real time estimation

  Abstract Flash floods are one of the most common natural disasters worldwide, causing thousands of casualties every year. A combination of fixed and mobile sensors can be used to track these events. In particular, the emergence of Unmanned Aerial Vehicles (UAVs) could enable the monitoring of floods over large geographical areas. In this talk, we focus on the problems of sensing, data assimilation, and trajectory planning for a swarm of mobile agents (for example UAVs) sensing floods We first show that deep-learning can efficiently approximate the flood evolution over time, given external
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  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.
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  Modelling and Simulation of Biological Systems - 2022-11-28

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  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.
• 

  Towards Enhancing Security in Intelligent Surface-enabled Wireless Networks

  Prof. Galymzhan Nauryzbayez, Nazarbayev University, Kazakhstan

  Nov 28, 10:45 - 11:30

  B2 5220

  wireless networks

  Abstract The rampage of incessant cyber attacks have caused the disclosure of billions of users’ private data. In response, various data privacy laws and regulations have emerged, forcing the academia and industry to change their practice and bringing the demand for large-scale secure wireless networks to the spotlight. Such a demand, however, cannot be met by the state-of-the-art cryptographic techniques. To narrow the gap, recent years have seen rapid progress in physical layer security (PLS) solutions. In this talk, I will present our research on understanding and addressing the security
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  Formal Verification of Neural Network Controlled Cyber-Physical Systems

  Prof. Yasser Shoukry, University of California, Irvine, USA

  Nov 28, 10:00 - 10:45

  B2 5220

  Resilient

  Abstract Deep Neural Networks (DNNs) are increasingly being used to control physical/mechanical systems. Self-driving cars, drones, and smart cities are just examples of such systems to name a few. However, regardless of the explosion in the use of DNNs within a multitude of cyber-physical systems (CPS) domains, the safety, and reliability of these DNN-controlled CPS is still an understudied problem. Mathematically based techniques for the specification, development, and verification of software and hardware systems, also known as formal methods, hold the promise to provide appropriate

Nov 20 - Dec 4, 2022

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  Model-based Design of Cyber-physical Systems with Learning Components

  Prof. Panagiotis Katsaros, Aristotle University of Thessaloniki

  Nov 27, 14:30 - 15:15

  B2 5220

  cyber-physical systems

  Abstract Cyber-physical system design involves heterogeneous components for sensing, control, actuation, networking and relies on models for the system’s physical dynamics, and all interactions with its environment. Since safety and security are critical, model-based engineering methods are often used to increase the confidence and guide the validation testing of the system under design. An abundant of results from modern system theory (discrete, continuous and hybrid) provides the foundations for modeling, simulation, testing and formal verification. Mature tools and languages offer advanced
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  Cross-Layer Security of Embedded and Cyber-Physical Systems

  Prof. Mohammad Alfaruque, University of California, Irvine, USA.

  Nov 27, 13:45 - 14:30

  B2 5220

  cyber-physical systems

  Abstract Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components . Embedded systems comprising of hardware and software systems are the major enabling technology for these cyber-physical systems. Today, CPSs can be found in security-sensitive areas such as aerospace, automotive, energy, healthcare, manufacturing transportation, entertainment, and consumer appliances. Compared to the traditional information processing systems, due to the tight interactions between cyber and physical components
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  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.
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  Hardware security and trust for wireless integrated circuits

  Prof. Haralampos Stratigopoulos, Sorbonne Université

  Nov 27, 10:15 - 11:30

  B2 5220

  Hardware Network wireless

  Abstract We will discuss Integrated Circuit (IC) security and trust threats and corresponding countermeasures with a focus on wireless ICs. We will present solutions to protect RF transceiver ICs against piracy attempted by malicious System-on-Chip (SoC) integrators, foundries, or end-users via reverse-engineering. We will also present hardware Trojan attacks on RF transceivers aiming at creating a covert channel for stealing sensitive information, i.e., cypher keys. Brief Biography Haralampos-G. Stratigopoulos received the Diploma in electrical and computer engineering from the National
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  Trust, But Verify: Towards Self-Aware, Safe, Autonomous Self-Driving Systems

  Prof. Fadi Kurdahi

  Nov 27, 09:30 - 10:15

  B2 5220

  Autonomous system

  Abstract Mobile autonomous devices use sophisticated algorithms to automatically navigate diverse physical environments in a safe manner. Guaranteeing system safety while optimizing resource-usage of an autonomous device’s computer is an outstanding challenge. Runtime verification helps provide robustness by monitoring application execution at runtime. These systems operate in dynamic environments, requiring self-aware runtime management to maximize lifetime while enforcing safe execution. Safe execution and lifetime management require consideration of performance and efficiency, two
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  Embedded and Cyber Connected Systems (ECCS) Workshop

  Prof. Ahmed Eltawil, Prof. Charalambos Konstantinou, Prof. Khaled Nabil Salama

  Nov 27, 08:00 - Nov 28, 17:00

  B2 L5 R5220

  cybersecurity machine learning 5G networks hardware security

  The workshop aims to bring together experts to present their latest research efforts related to Embedded and Cyber Connected Systems architectures and platforms that can scale efficiently, as well as operate securely and resiliently to provide the necessary resources demanded by current and future network applications.

Nov 20 - Nov 27, 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).
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  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.
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  Lipschitz learning and the infinity-Laplacian

  Miguel Urbano, Professor, Applied Mathematics and Computational Sciences

  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).
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  On training algorithms for neural networks

  Jinchao Xu, Professor, Applied Mathematics and Computational Sciences

  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

Nov 13 - Nov 27, 2022

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  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 20, 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.
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  Functional Differential Equations in Models of Collective Behavior - Graduate Seminar

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

  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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  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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  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.

Nov 6 - Nov 20, 2022

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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 13, 2022

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  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.
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  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.
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  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.
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  Modeling water waves: from tsunamis to the kitchen sink

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  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.
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  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 Sciences

  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.