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...

Applied Mathematics Colloquium - Sebin Gracy

Oct. 27, 2023

Sebin Gracy, Rice Academy of Fellows, Rice University Spreading processes over networks The talk focuses on mathematical epidemiology, or, more broadly, on spreading processes. Spreading processes are observed in several settings. Prominent examples include the spread of viruses, in particular, pandemics such as COVID-19, Spanish flu; spread of opinions on...

Applied Mathematics Colloquium - Daniel Gomez

Oct. 20, 2023

Daniel Gomez, Center for Mathematical Biology, University of Pennsylvania Asymptotic Analysis of Singularly Perturbed Problems with Lévy Flights What does a reaction-diffusion system where one species has an asymptotically small diffusivity have in common with the problem of finding the average time for a Brownian particle to first hit an...

Applied Mathematics Colloquium - Gadi Fibich

Oct. 13, 2023

Applied Mathematics Colloquium - Gadi Fibich Gadi Fibich, Department of Applied Mathematics, Tel Aviv University TBA TBA More information about this speaker may be found at https://www.tau.ac.il/~fibich/

Applied Mathematics Colloquium - Juliane Mueller

Oct. 6, 2023

Juli Mueller; Artificial Intelligence, Learning, and Intelligent Systems (ALIS) group; National Renewable Energy Laboratory (NREL) Derivative-free Optimization Algorithms using Surrogate Models and Active Learning Many scientific domains rely on computational simulation models to approximate physical phenomena under study. In some cases these simulations have parameters that must be tuned such...

Applied Mathematics Colloquium - Emily King

Sept. 29, 2023

Emily King, Department of Mathematics, Colorado State University Interpretable, Explainable, and Adversarial AI: Data Science Buzzwords and You (Mathematicians) Many state-of-the-art methods in machine learning are black boxes which do not allow humans to understand how decisions are made. In a number of applications, like medicine and atmospheric science, researchers...

Applied Mathematics Colloquium - Madeleine Udell

Sept. 22, 2023

Madeleine Udell, Institute for Computational and Mathematical Engineering, Stanford University Low rank approximation for faster optimization Low rank structure is pervasive in real-world datasets. This talk shows how to accelerate the solution of fundamental computational problems, including eigenvalue decomposition, linear system solves, composite convex optimization, and stochastic optimization (including deep...

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