I am a statistician from Merck & Co. I work in the Methodology Research group led by Keaven M. Anderson in BARDS. My focus is at the intersection of statistical methodology research and software architecture innovation. I currently serve on the R Consortium Infrastructure Steering Committee as a representative of Merck & Co., as well as on the R Submission Working Group.

My research interests include sparse linear models, representation learning, and computational reproducibility. I build software in R to automate my workflow. My favorites include msaenet, oneclust, liftr, ggsci, and pkglite.

Previously, I worked as a data scientist at Seven Bridges in Boston. Earlier in my career, I studied human genetics in Matthew Stephens Lab at the University of Chicago. I earned my PhD degree in statistics from Central South University, China. My thesis focused on developing statistical learning methods for high-dimensional data analysis, advised by Qing-Song Xu.