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
Nabarun Deb (University of Chicago)
Hyungju Hwang (POSTECH)
Jakwang Kim (University of British Columbia)
Tongseok Lim (Purdue University)
Hanbaek Lyu (University of Wisconsin-Madison)
Dan Mikulincer (MIT)
Chenchen Mou (City University of Hong Kong)
Asuka Takatsu (Tokyo Metropolitan University)
Xiaolu Tan (Chinese University of Hong Kong)
Andrew Warren (University of British Columbia)
Chulhee Yun (KAIST)
Se-Young Yun (KAIST)
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