Seminars

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...

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...

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...

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 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/

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...

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...

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...

Bradley Warner, Director of Data Science Program, US Air Force Academy Data Science Education: A Journey of Creation and Implementation In the increasingly data-driven landscape of decision-making, the demand for skilled data scientists is on the rise. Therefore, the development and execution of data science education programs have become essential...

Maria Kazachenko, Laboratory for Atmospheric and Space Phenomena (LASP), University of Colorado Boulder Solar Magnetic Fields Before and During Eruptions: Perspectives with the Largest Telescope to observe the Sun (DKIST) Space weather is largely caused by the activity of our Sun. Invisible yet powerful magnetic fields, created within the Sun,...