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Lecture on AI Flagship 2018-05-23T18:10:30+00:00

2018 AI World Cup Lecture on AI Flagship 1

” Sample-efficient deep reinforcement learning “

Prof. Sae-Young Chung

KAIST

Biography

Sae-Young Chung received the B.S. and M.S. degrees in electrical engineering from Seoul National University, Seoul, South Korea, in 1990 and 1992, respectively and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2000. From September 2000 to December 2004, he was with Airvana, Inc., Chelmsford, MA, USA. Since January 2005, he has been with the School of Electrical Engineering, KAIST, Daejeon, South Korea, where he is currently Professor. He served as Associate Editor for the IEEE Transactions on Communications from 2009 to 2013 and for the IEEE Transactions on Information Theory from 2014 to 2016. He served as the Technical Program Co-Chair of the 2014 IEEE International Symposium on Information Theory and as the Technical Program Co-Chair of the 2015 IEEE Information Theory Workshop. His research interests include information theory, deep learning, and reinforcement learning.

2018 AI World Cup Lecture on AI Flagship 2

” Brain-inspired AI : engineering the robot minds “

Prof. Sang Wan Lee

KAIST

Biography

Sang Wan Lee is currently an assistant professor with the department of bio and brain Engineering at KAIST, and the director of the laboratory for brain and machine intelligence (http://aibrain.kaist.ac.kr). In 2009, he received Ph.D. in Electrical Engineering and Computer Science from KAIST. During 2010-2015, he was a postdoctoral associate at Mcgovern institute for brain research at MIT, followed by a Della Martin postdoctoral scholar in the Computation & Neural Systems and the Behavioral & Social Neuroscience program at Caltech. He was the recipient of the Della-Martin fellowship (2014) and the Google faculty research award in computational neuroscience (2017). His research interests include brain-inspired artificial intelligence and computational neuroscience.