Applied Math Seminar

Title: Controlling the unmanageable: insight into control methods for biological systems Abstract: When formulating a model for a biological system, often we want to use the model to understand the […]

Title: How well do neurons, humans, and artificial neural networks predict? Abstract: Sensory prediction is thought to be vital to organisms, but few studies have tested how well organisms and […]

Title: TBD Abstract: TBD

Title: Mathematical models studying the effectiveness of control strategies for malaria Abstract: According to the 2023 World Malaria Report: Nearly half the world's population lives in areas at risk of […]

Title: Designing Robust Networks of Coupled Phase Oscillators with Applications to the High-Voltage Electric Grid Abstract: A network is any collection of objects, called nodes, in which some pairs of […]

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 […]

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 […]

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 […]

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. Our primary analysis focuses on the top marginal personal income and corporate income tax, and their effect on top incomes and top income shares within […]

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 that the data is sampled from an unknown submanifold of a high dimensional Euclidean space. A great deal of research deals with obtaining information about […]