The bulk of my research examines problems that arise in the context of high dimensional regression, where the number of regression coefficients is quite large relative to the number of observations and strong assumptions must be made to obtain practically useful estimates. In particular, I have explored the usefulness of specific assumptions on the tail behavior and/or known structural relationships relating the unknown regression coefficients from both Bayesian and frequentist perspectives.
I am especially fond of problems that are computationally challenging!
This has led me to broaden my research interests to include methods for data that are correlated over time and/or space. Specifically:
I just joined the Department of Mathematics and Statistics at the University of Massachusetts Amherst as an assistant professor! I grew up in Massachusetts, went to college at the University of Chicago, spent the first three years of graduate school in Seattle and the last two in Durham, North Carolina, and did a postdoc at Cornell in the Center for Applied Mathematics. I received my Ph.D. in Statistics from the University of Washington in 2018 under the supervision of Peter Hoff. After that, David Matteson and Gennady Samorodnitsky tried their best to teach me all about time series!