event

The main goal of this summer school is to expose junior researchers to the exciting research opportunities in diverse topics arising from optimal transport, stochastic analysis, and their applications to machine learning. Participants will have various opportunities to get inspired and to interact with others through lectures, discussions, and social activities.

TBA

Organizers Inwon Kim (University of California, Los Angeles, Department of Mathematics) Katy Craig (University of California, Santa Barbara, Department of Mathematics) Yunan Yang (Cornell University, Department of Mathematics) Li Wang (University of Minnesota, Department of Mathematics) Yanqin Fan (University of Washington, Department of Economics) Confirmed Speakers Jingwei Hu (University of Washington, Department of Applied Mathematics) Olga Turanova (Michigan State University, Department of Mathematics) Ruiyu Han (Carnegie Mellon, Mathematical Sciences) Caroline Moosmuller (University of North Carolina, Mathematics) Christina Frederick (New Jersey Institute of Technology, Mathematical Sciences) Elisa Negrini (University of California, Los Angeles, Department of Mathematics) Silvana Pesenti (University of Toronto, Department of Statistical Sciences) Xiaohong Chen (Yale University, Department of Economics) Ruidi Chen (Microsoft) Yuan Gao (Purdue University, Department of Mathematics) Siting Liu (University of California, Los Angeles, Department of Mathematics) Jiao Jiao Fan (Georgia Institute of Technology, Aerospace Engineering) Maria Oprea (Cornell University, Mathematics) Yao Xie (Georgia Institute of Technology, H.

Density functional theory (DFT) is one of the workhorses of quantum chemistry and material science. In principle, the joint probability …

The Gromov-Wasserstein (GW) distance quantifies dissimilarity between metric measure (mm) spaces and provides a natural alignment …

I will try to present an overview of some results of unbalanced optimal transport for positive measures with different total masses, …

Shifted divergences provide a principled way of making information theoretic divergences (e.g. KL) geometrically aware via optimal …

Deep learning-based approaches have succeeded surprisingly in various fields of sciences. In particular, one of the first and most …