Events

Nov 26 - Nov 20, 2022

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 19 - Nov 13, 2022

Nov 12 - Nov 6, 2022

Nov 5 - Oct 30, 2022

Oct 29 - Oct 23, 2022

Enabling the New Semiconductor Revolution through Advances in Design-for-Yield, Design-for-Test, and Fault-Origin Analysis

Krish Chakrabarty, John Cocke Distinguished Professor, Electrical and Computer Engineering, Duke University

Oct 26, 11:15 - 12:15

B9 L2 R2322

advanced semiconductors

In this talk, I will first provide an overview of our research on design-for-testability of 3D integrated circuits, silicon lifecycle management, microfluidics, and hardware security, which all involve close industry collaborations. I will next describe in more detail our recent work on design-for-yield that targets manufacturing imperfections for layouts based on emerging carbon nanotube field-effect transistors. Following this, I will present our ongoing work on built-in self-test of monolithic 3D integrated circuits. Finally, I will describe a test and diagnosis technique to characterize fault origins in inter-tier vias and resistive random-access memories for monolithic 3D integration.
On Some PDEs with singular data

Prof. Mohammed Kbiri Alaoui, King Khalid University

Oct 25, 15:30 - 17:00

B1 L3 R3119

PDEs singular data

The aim of this talk is to investigate the following non local p-Laplacian problem with data a bounded Radon measure.
Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

Zhongwei Zhang, Ph.D. Student, Statistics

Oct 25, 15:00 - 18:00

B5 L5 R5220

Statistics of extremes spatial statistics

This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.
A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Public Colloquium

Andrea Fratalocchi, Professor, Electrical and Computer Engineering

Oct 25, 14:00 - 16:00

B9 L2 R2322

Applied complexity artificial intelligence information security biomedical applications

Applied Complexity aims to understand the physical origin of these behaviors and transform them into sustainable technologies that tackle global problems of global interest. These range from energy harvesting to clean water production, the design of smart materials, biomedical applications, information security, artificial intelligence, and global warming. 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.
Mean-Field Games on Networks and Wardrop Equilibria - Graduate Seminar

Ricardo De Lima Ribeiro, Research Specialist, CEMSE, KAUST

Oct 25, 12:00 - 13:00

B9 L2 R2322

mean-field games

Models for flows on networks arise in the study of traffic and pedestrian crowds. These models encode congestion effects, the behavior and preferences of agents, such as aversion to crowds and their attempts to minimize travel time. We will present the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field game.
Applied AI for sustainable development

Prof.Evgeny Burnaev, Applied AI Center, Skolkovo Institute of Science and Technology

Oct 24, 12:00 - 13:00

B9 L2 R2322 H1

Skoltech machine learning Bayesian modeling artificial intelligence

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.
BioChips: Leveraging Integrated Electronics for Sensing, Medicine, and Science

Pamela Abshire, Professor, Department of Electrical and Computer Engineering and Institute for Systems Research at the University of Maryland, College Park

Oct 23, 12:00 - 13:00

B9 L2 R2322

biomedical electronics Biosensors

This talk will provide a brief overview of LoCMOS systems, the technologies used to construct them, and their application to novel applications in biosensing, medical diagnostics, and neuroscience. The integration of integrated circuits into LoCMOS devices poses a number of distinct and vexing challenges, increasing complexity while reducing the need for external instrumentation.

Oct 22 - Oct 16, 2022

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

Oct 20, 15:00 - 16:00

B9 L2 H2

Reinforcement Learning

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

Oct 19, 16:00 - 17:00

B9 L2 H2

Reinforcement Learning Digital Behavioral Health

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
Gradient Estimates for weak solutions of Elliptic PDE's - Colloquium

Giuseppe Di Fazio, Professor, University of Catania

Oct 18, 15:30 - 17:00

B1 L3 R3119

Elliptic PDE

In the Seminar, we exploit what is the heart of the technique to show gradient estimates allowing the function ? to belong to very general function spaces. Our technique is very flexible and allows us to show the existence, uniqueness, and well-posedness of the Dirichlet problem in several classes.
Distributed multi-GPU Algorithms for Hierarchical Matrices

George Turkiyyah, Research Professor, Applied Mathematics and Computational Science

Oct 18, 12:00 - 13:00

B9 L2 R2322

GPU Algorithms hierarchical matrices

In this talk, we show that, besides their optimal O(N) algorithmic complexity, hierarchical matrix operations also benefit from parallel scalability on distributed machines with extremely large core counts. In particular, we describe high-performance, distributed-memory, GPU-accelerated algorithms for matrix-vector multiplication and other operations on hierarchical matrices in the H^2 format.
Recent advances in mixed finite element approximation for poroelasticity

Arbaz Khan, Assistant Professor, Department of Mathematics, Indian Institute of Technology (IIT)

Oct 18, 11:00 - 12:00

B1 L3 R3119

parallel computing Numerical Solution of Partial Differential Equations stochastic differential equations

This talk discusses the non-conforming approximation of Biot's consolidation model. In the first part of the talk, we discuss posteriori error estimators for locking-free mixed finite element approximation of Biot’s consolidation model. In the second part of the talk, we discuss a novel locking-free stochastic Galerkin mixed finite element method for the Biot consolidation model with uncertain Young’s modulus and hydraulic conductivity field.

Oct 15 - Oct 9, 2022

Oct 8 - Oct 2, 2022