Title: Randomly pivoted Cholesky: Fast, accurate matrix approximation for scientific machine learning Abstract: Low-rank approximation of positive semidefinite matrices is a basic problem in computational mathematics, with many applications to machine learning and scientific computing. Existing approaches for this problem largely fall into two categories: simple, fast, but sometimes inaccurate methods and sophisticated, slower methods […]

Title: Erlang-Distributed SEIR Epidemical Models Abstract: We examine the effects of different dynamics in epidemiological models, focusing on two key approaches. The first model incorporates reaction-diffusion dynamics, where susceptible individuals avoid areas with high concentrations of infected individuals. The second model divides exposed and infectious individuals into symptomatic and asymptomatic subclasses. Our findings emphasize the […]

Title: Modeling viral STI epidemics Abstract: We will describe an SIR model of viral sexually transmitted infections in a population structured by sex and sexual preference and its validation in the simple SI case from HIV data incidence. We will also use the model to establish a plausible structure of the U.S. population by sexual […]

Title:    Randomized Kaczmarz Methods for Corrupted Tensor Linear Systems Abstract: Recovering tensor-valued signals from corrupted measurements is a central problem in various applications such as hyperspectral image reconstruction and medical imaging. This talk considers tensor linear systems of the form AX = B, that contain observations potentially affected by sparse, large-magnitude corruptions.  A quantile-based randomized […]