In this project, the investigator and his colleagues will study selected topics in high-dimensional statistics and data science. This work will help scientists working in biotechnology and other areas, who generate large scale datasets, to interpret and uncover the important patterns in their data. This should help scientists and doctors to discover the biological bases of many diseases, and improve prognosis and treatment selection for patients.
Specifically, these projects will include an interaction model for high-dimensional regression and classification, a statistical model for studying mouse and human genomic comparisons, a selective inference approach to average treatment effect estimation (ATE), and a research monograph on post-selection inference in statistics.
