Seminars

Applied Mathematics Colloquium - Mark Ward

March 8, 2024

Mark Ward, Department of Mathematics, Purdue University The Data Mine Model for Partnerships The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data-intensive experience embedded in a large learning community, The Data Mine...

Applied Mathematics Colloquium - Leonid Berlyand

March 1, 2024

Leonid Berlyand, Department of Mathematics, Penn State University Enhancing Accuracy in Deep Learning Using Random Matrix Theory We discuss applications of random matrix theory (RMT) to the training of deep neural networks (DNNs). Our focus is on the pruning of DNN parameters, guided by the Marchenko-Pastur spectral RMT approach. Our...

Applied Mathematics Colloquium - Chad Topaz

Feb. 23, 2024

Chad Topaz, Professor of Complex Systems, Williams College Data Science for Criminal and Social Justice Tens of millions of people in the United States have been directly impacted by the criminal justice system, with nearly half the population affected through close familial or social ties. Alongside the direct harm inflicted...

Applied Mathematics Colloquium - Pascale Garaud

Feb. 16, 2024

Pascale Garaud; Department of Applied Mathematics; University of California, Santa Cruz Regimes of stratified turbulence across parameter space: from asymptotic analysis to DNS In this talk I will present recent theoretical and numerical progress in modeling the dynamics of stratified turbulence in regimes appropriate of the Earth's atmosphere and oceans,...

Applied Mathematics Colloquium - Donna Calhoun

Feb. 9, 2024

Donna Calhoun, Department of Mathematics, Boise State University Coupling scientific software through the adaptive tree-based library ForestClaw Coupling scientific research codes presents several challenges. The codes may not be on the same mesh, so results from each simulation must be communicated between meshing environments. Each simulation likely comes with its...

Applied Mathematics Colloquium - Gunilla Kreiss

Jan. 26, 2024

Gunilla Kreiss, Department of Information Technology, Uppsala University Cut FEM meets finite differences There is a cut-FEM methodology with ghost penalty stabilization, which can be applied to hyperbolic conservation laws and wave equations, and allows for using Cartesian grids. Explicit time-stepping is preferable for hyperbolic problems. The stabilization will ensure...

Applied Mathematics Colloquium - Lise-Marie Imbert-Gérard

Jan. 19, 2024

Lise-Marie Imbert-Gérard, Department of Mathematics, The University of Arizona Numerical simulation of wave propagation around planes The design of modern aircrafts involves a balance of competing cost and performance requirements, usually combining experimental and theoretical approaches. The propagation of waves around aircrafts is one of many components studied in this...

Applied Mathematics Colloquium - Heather Zinn Brooks

Dec. 1, 2023

Heather Zinn Brooks, Department of Mathematics and Barbara Stoke Dewey Assistant Professor of the Life Sciences, Harvey Mudd College Emergence of polarization in a sigmoidal bounded-confidence model of opinion dynamics We propose a nonlinear bounded-confidence model (BCM) of continuous time opinion dynamics on networks with both persuadable individuals and zealots...

Applied Mathematics Colloquium - Daniele Avitabile

Nov. 10, 2023

Daniele Avitabile, Amsterdam Center for Dynamics and Computation, Vrije Universiteit Amsterdam Uncertainty Quantification for Neurobiological Networks This talk presents a framework for forward uncertainty quantification problems in spatially-extended neurobiological networks. We will consider networks in which the cortex is represented as a continuum domain, and local neuronal activity evolves according...

Applied Mathematics Colloquium - Malena Español

Nov. 3, 2023

Malena Español, School of Mathematical and Statistical Sciences, Arizona State University Computational Methods for Inverse Problems in Imaging Discrete linear and nonlinear inverse problems arise from many different imaging systems. These problems are ill-posed, which means, in most cases, that the solution is very sensitive to the data. Because the...

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