Overview of Optimal Transport Methods for Density Function Approximation
TIME: 3:30 PM
LOCATION: Virtual Zoom Seminar
SPEAKER: Vanessa Lopez-Marrero, Computational Scientist, Brookhaven National Laboratory
ABSTRACT:
Optimal transport, broadly defined, deals with the problem of minimizing the cost of transporting one (probability) measure to another. It is of interest in a range of subjects such as probability theory, optimization, and partial differential equations, among others. It is also used in many applications including machine learning, Bayesian inference, and sampling. In this talk we will give an overview of optimal transport, in relation to (probability) density function estimation, with emphasis on computational methods for constructing optimal transport maps. We will also discuss our ongoing work concerning the use of transport maps for estimating unknown density functions characterizing data,specifically when the available data is scarce (“small data” scenarios).
Bio: Vanessa Lopez-Marrero is a Computational Scientist at the Brookhaven National Laboratory. Her general research interests lie in the areas of computational and applied mathematics and dynamical systems. She holds a Ph.D. in Scientific Computing from the Computer Science Department, University of Illinois at Urbana-Champaign, with a Certificate of Specialization in Computational Science and Engineering.
HOST: Jose Castillo (CSRC) and the Sustainable Horizons Institute CRLC Virtual Seminar Series