I am very interested in developing useful statistical methodology for practitioners in a variety of scientific and industrial fields. I am particularly interested in the tradeoffs between robustness and efficiency in estimation.

I am interested in a wide variety of disciplines within Statistics. These include, but are not limited to, maximum likelihood estimation, exponential family theory, generalized linear models, model averaging, envelope methodology, conformal prediction, causal inference, bootstrap techniques, and multivariate statistics.

UIUC Statistics graduate students are encouraged to reach out to me if you are looking for research opportunities. I currently have several methodological and programming projects in robust efficient prediction methods, variance reduction for estimation of vector-valued parameters, post shock prediction, careful prior construction in Bayesian Statistics and baseball.

URES opportunity I am looking for a student to research the evolving interest of baseball since the inception of the MLB. The purpose of this work is to develop statistical methodology that more objectively compares players across eras. The ideal student should be able to think critically, work fairly independently, and should have a broad interest in history and data-sceince. A career in baseball analytics will likely not come out of this work, but the student will develop a deep and nuanced understanding of statistical modeling with successful completion of this project.