Seminar by Dr. Chengchun SHI from London School of Economics and Political Science

Reinforcement learning is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. It has been arguably one of the most vibrant research frontiers in machine learning over the last few years. Nevertheless, statistics as a field, as opposed to computer science, has only recently begun to engage with reinforcement learning both in depth and in breadth. In today's talk, I will discuss some of my recent work on developing statistical inferential tools for reinforcement learning.

Chengchun Shi is an Assistant Professor of Data Science at London School of Economics and Political Science. His research is concerned with reinforcement learning and analysis of complex data, with applications to precision medicine, mobile health, ridesharing and neuroimaging. He was the recipient of the Royal Statistical Society Research Prize this year. He also received the IMS travel award in two consecutive years. He has over 10 papers accepted at Annals of Statistics, Journal of Americal Statistical Association, Journal of the Royal Statistical Society (Series B) and International Conference on Machine Learning.