Summer school on Optimal Transport, Stochastic Analysis and Applications to Machine Learning
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.
Date & Venue
June 3 (Mon) - June 7 (Fri), 2024
Korea Advanced Institute of Science & Technology (KAIST)
All participants should register to attend. Please register by March 31 via the following link: Registration
Lecture Series
Beatrice Acciaio (ETH Zurich)
Sinho Chewi (Institute for Advanced Study)
Dejan Slepčev (Carnegie Mellon University)
Feng-Yu Wang (Tianjin University)
Invited Talks
Brendan Pass (University of Alberta)
Chenchen Mou (City University of Hong Kong)
Hanbaek Lyu (University of Wisconsin-Madison)
Se-Young Yun (KAIST)
Dan Mikulincer (MIT)
Asuka Takatsu (Tokyo Metropolitan University)
Hyungju Hwang (POSTECH)
Jakwang Kim (University of British Columbia)
Andrew Warren (University of British Columbia)
Tongseok Lim (Purdue University)
Chulhee Yun (KAIST)
Nabarun Deb (University of Chicago)
Organizers
Zhen-Qing Chen (University of Washington), Wanmo Kang (KAIST), Young-Heon Kim (University of British Columbia), Jioon Lee (KAIST), Kyeongsik Nam (KAIST), Soumik Pal (University of Washington), Brendan Pass (University of Alberta)
Contact information
Kyeongsik Nam (ksnam90@gmail.com), Young-Heon Kim (yhkim@math.ubc.ca), Soumik Pal (soumikpal@gmail.com), SAARC (saarc@kaist.ac.kr)
Acknowledgement
Korea Advanced Institute of Science & Technology
Pacific Institute for the Mathematical Sciences
Stochastic Analysis & Application Research Center