[Abstract]

Over the past few decades, optimal transport theory has attracted growing interest across a range of fields, including partial differential equations, probability, and machine learning. Our discussion begins by introducing the basic theory of optimal transport. We then explore its diverse applications in various machine learning problems, with a particular focus on generative models, including diffusion models.

[Biography]

Dohyun Kwon is an Assistant Professor in the Department of Mathematics at the University of Seoul and an Affiliate Professor at the Korea Institute for Advanced Study (KIAS). His research interests are mathematical machine learning, partial differential equations, and optimization.
Before joining the University of Seoul, he was a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison. He obtained his Ph.D. in Mathematics from UCLA in 2020 and received B.S. in Mathematical Sciences from Seoul National University. He is a recipient of the Kwanjeong Educational Foundation Fellowship for doctoral study, the Beckenbach Award, and the POSCO Science Fellowship from the TJ Park Foundation.