Title：On Statistical Learning for Individualized Decision Making with Complex Data

报告时间：2019年8月29日上午9点30

地点：信息楼407

Author: Shi Chengchun 

Abstract: In precision medicine, individualizing the treatment decision rule can capture patients' heterogeneous response towards treatment.In finance, individualizing  the investment decision rule can improve individual's financial well-being. In a ride-sharing company, individualizing the order dispatching strategy can increase its revenue and customer satisfaction. With the fast development of new technology, modern datasets often consist of massive observations, high-dimensional covariates and are characterized by some degree of heterogeneity.

In this talk, I will present my research on individualized decision making with modern complex data. The talk is divided into two parts. First, I will focus on the data heterogeneity and introduce a new maximin -projection learning for recommending an overall individualized decision rule based on the observed data from different populations with heterogeneity in optimal individualized decision making.In the second part, I will briefly summarize the statistical learning methods I've developed for individualized decision making with complex data and discuss my future research directions. 

报告人简介：史成春，2019年获美国北卡罗来纳州立大学博士学位，即将成为伦敦政治经济学院教授。对统计与机器学习、个性化处理、高维数据建模等有理论和应用意义的科学问题，开展了若干极有价值的研究工作。在包括《Journal of the Royal Statistical Society, Series B》、《Annals of Statistics》、《Journal of the American Statistical Association》和《Journal of Machine Learning Research,》等在内的国际顶级期刊发表论文十余篇。