Date: Sunday June 5, 2022
Time: 11:00 - 17:00 (EDT)

Title: Information geometry and applications

Organizers: 

Leonard Wong, University of Toronto
Jun Zhang, University of Michigan

Description:

Information geometry studies the geometry of spaces of probability distribution, which are also known as statistical manifolds. It provides a unified mathematical framework to study objects such as entropy, KL-divergence, the Fisher-Rao metric, and exponential families, as well as their generalizations. It has been applied to statistics and machine learning among other fields, particularly due to its close connections with optimal transport and statistical physics.

Schedule (Eastern Daylight Times)

11am-12pm: Tutorial on information geometry - Jun Zhang (University of Michigan)
12pm-1pm: Lunch Break
1pm-1:30pm: Paul Marriot (University of Waterloo)
1:30pm-2pm: Guido Montufar (University of California, Los Angeles)
2pm-2:30pm: Gabriel Khan (Iowa State University)
2:30pm-2:45pm: Break
2:45pm-3:15pm: Melvin Leok (University of California, San Diego)
3:15pm-3:45pm: Tian Han (Stevens Institute of Technology)
3:45pm-4:15pm: Wuchen Li (University of South Carolina)
4:15pm-4:45pm: Leonard Wong (University of Toronto)
4:45pm-5pm: Discussion