Reinforcement Learning
Algorithm & Application
Virtual Seminar

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Welcome

This is an online seminar that presents the latest advances in reinforcement learning applications and theory. Our goal is to bring you a virtual seminar (approximately) featuring the latest work in applying reinforcement learning methods in many exciting areas (e.g., health sciences, or two-sided markets).

Topics

Precision Health

E-Commerce

Two-Sided Markets

Robotics

Others

Education
Medical Imaging
Game

Theory & Algorithm

Speakers

Susan Susan Murphy Susan Murphy is Professor of Statistic at Harvard University, Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University, and Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her lab works on clinical trial … Bo An Bo An Bo An is a President’s Council Chair Associate Professor in Computer Science and Engineering, Nanyang Technological University, Singapore. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His current research … Michael Michael R. Kosorok Michael R. Kosorok, Ph.D., the W.R. Kenan, Jr. Distinguished Professor of Biostatistics and Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill, received his PhD in Biostatistics from the University of Washington in 1991. He is an internationally known biostatistician… Tony Qin Tony Qin Tony Qin is Principal Research Scientist and Director of the reinforcement learning group at DiDi AI Labs, working on core problems in ridesharing marketplace optimization. Prior to DiDi, he was a research scientist in supply chain and inventory optimization at Walmart Global E-commerce... Yansheng Yansheng Wang Yansheng Wang is currently a first year Ph.D. candidate in School of Computer Science and Engineering at Beihang University. He is working on crowd intelligence, spatial crowdsourcing and reinforcement learning. He has published several papers in highly refereed conferences ... Fanyou Wu Fanyou Wu  Fanyou Wu is now a Ph.D. candidate in Forestry and Natural Resources Department, Purdue University. His research focuses on the application of machine learning in forestry and transportation, and has published several paper in those fields. He has also won many championships and runners-up ... Eric Laber Eric Laber Eric Laber is the Goodnight Distinguished Professor and Faculty Scholar in the department of Statistics at NC State University. He joined NC State after completing his PhD at the University of Michigan in 2011. His research focuses on methods development for data-driven decision making ... Nathan Nathan Kallus Nathan Kallus is an Assistant Professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. Nathan’s research interests include personalization; optimization, especially under uncertainty; causal inference; sequential decision making... Chengchun Chengchun Shi Chengchun Shi is Assistant Professor of Data Science at London School of Economics and Political Science. His research interests include: (i) statistical methods in reinforcement learning; (ii) statistical analysis of complex data. Despite his age, he has over 10 papers published/accepted ... Nick Nathan Kallus Nick Rhinehart is a Postdoctoral Scholar in the Electrical Engineering and Computer Science Department at the University of California, Berkeley with Sergey Levine. His work focuses on fundamental and applied research in machine learning and computer vision for behavioral ...
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Recent Events

May 26th, 2021

10:30 – 12:00pm EST

Zhuoran Yang, Princeton University

On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Space

Zhaoran Wang, Northwestern University

Is Pessimism Provably Efficient for Offline RL?

Houssam Nassif, Amazon

Solving Inverse Reinforcement Learning, Bootstrapping Bandits

May 13th, 2021

10:30pm – 11:30pm EST

Guanjie Zheng, Shanghai Jiao Tong University

Improving Urban Traffic Signal Control via Reinforcement Learning


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