The focus of this seminar is various methods in scientific computing, the analysis of their convergence properties and computational efficiency, and their adaptation to specific applications.

The seminar meets Mondays, 1:30-2:20 pm, over Zoom. Meeting details are included in the weekly seminar announcements.

Date	Speaker and Title
Feb 15	Nate Veldt (Cornell University) Generalized Hypergraph Cuts: from Theoretical Foundations to Applications
Feb 22	Benjamin Seibold (Mathematics, Temple University) Spatial Manifestations of Order Reduction, and Overcoming it via Weak Stage Order
Mar 01	Haizhao Yang (Mathematics, Purdue) Reproducing Activation Functions for Deep Learning
Mar 08	Christopher Musco (New York University) Sublinear Time Spectral Density Estimation
Mar 15	TBA
Mar 22	Estelle Massart (University of Oxford and National Physical Laboratory) Exploiting geometric structure in the data/parameter space for machine learning
Mar 29	TBA
Apr 05	Donsub Rim (NYU) Model reduction of nonlinear wave propagation. Reduced deep networks, depth separation and the Radon transform
Apr 12	Peng Chen (Oden Institute, UT Austin) Projected Variational Methods for High-dimensional Bayesian Inference
Apr 19	Elizabeth Qian (Department of Computing and Mathematical Sciences (Caltech)) Reduced operator inference for nonlinear PDEs
May 03	Song Mei (Statistics, UC Berkeley) The efficiency of kernel methods on structured datasets
May 10	Ahmed El Alaoui (Statistics and Data Science, Cornell University) Algorithmic advances on the negative spherical perceptron problem