• Applied Math Seminar: Denis Gaidashev (Uppsala University)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    Title: Renormalization and wild attractors for Fibonacci maps Abstract: A Fibonacci map is a piecewise defined map of a subset of an interval I onto I with a unique critical […]

  • Applied Math Seminar: Ryan Aschoff (UC Riverside)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    Title: Smooth non-decaying solutions to the 2D dissipative quasi-geostrophic equations Abstract: In this talk we explore the two-dimensional dissipative surface quasi-geostrophic (SQG) equation with fractional diffusion of order 2α for […]

  • Applied Math Seminar: Efstratios Tsoukanis (Claremont Graduate University)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    Title: Bi-Lipschitz Invariants Abstract: Consider a finite-dimensional real vector space and a finite group acting unitarily on it. We investigate the general problem of constructing Euclidean stable embeddings of the quotient space of orbits. Our embedding relies on subsets of sorted coorbits with respect to chosen window vectors. Our main injectivity results examine the conditions […]

  • Applied Math Seminar: Sarah Robinson (Claremont McKenna College)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    Title: Do Taxes Affect Pre-Tax Income Inequality? Evidence from 100 Years of U.S. States Abstract: We study how U.S. state taxes have affected pre-tax income inequality during the last century. […]

  • Applied Math Seminar: Ryan O’Dowd (Claremont Graduate University)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    Title: Learning on manifolds without manifold learning Abstract: Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes […]

  • Applied Math Seminar: Ethan Epperly (Caltech)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    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 […]

  • Applied Math Seminar: Victoria Chebotaeva (USC)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    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 […]

  • Applied Math Seminar: Fabio Milner (Arizona State University)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    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 […]

  • Applied Math Seminar: Alejandra Castillo (Pomona College)

    Emmy Noether Room, Estella 1021, Pomona College, 610 N. College Ave., Claremont, CA, United States

    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 […]