Statistics And Data Science
I am a faculty member of the Department of Statistics and Data Science, a member of the Center for Applied Mathematics (CAM) and a member of the Machine Learning Group in CIS. As a member of the CIS Diversity and Inclusion Council, I am committed to promoting the diversity of the work force in data-science disciplines.
My research is broadly centered on statistical machine learning theory and high-dimensional statistical inference. I am interested in developing new methodology accompanied by sharp theory for solving a variety of problems in data science. Recent research projects include optimal transport for structured distributions, high-dimensional latent-space clustering, cluster-based inference, network modeling, inference in high dimensional models with hidden latent structure and topic models. I continue to be interested in the general areas of model selection, sparsity and dimension reduction in high dimensions, and in applications to genetics, systems immunology, neuroscience, sociology, among other disciplines.
My research is funded in part by the National Science Foundation (NSF-DMS). I am a Fellow of the Institute of Mathematical Statistics (IMS). I have served or am currently serving as an Associate Editor for a number of journals (the Annals of Statistics, Bernoulli, JASA, JRSS-B, EJS, the Annals of Applied Statistics).
A hitchhiker’s guide to data science contains some of my recent work and gets updated most frequently.
My office is 1184 Comstock Hall.