2026 Department Dissertation Awards

The Department of Statistics proudly announces the new cohort of our annual doctoral dissertation awards. Each exemplary distinction is accompanied by a prize of $1,000 and much congratulating at the Graduation Cake Celebration on June 11th.

Theodore W. Anderson Theory of Statistics Dissertation Award

Ran Xie – for her stable, scalable inference of covariance structures in multivariate linear mixed-effects models in the high-dimensional regime, utilizing random matrix theory to construct novel estimators and to establish their asymptotic properties via a new central limit theorem for linear spectral statistics.

Jerome H. Friedman Applied Statistics Dissertation Award

Amber Hu – for combining Bayesian state space modeling with scalable inference methods to create interpretable and stable latent dynamical system models that help experimental neuroscientists better understand how the brain represents and maintains persistent emotional states.

Ingram Olkin Interdisciplinary Research Dissertation Award

Xavier Gonzalez – for demonstrating how broad classes of state space models, long regarded as inherently sequential, can be evaluated efficiently in parallel, and for combining deep theoretical insight with scalable computational methods, opening important new directions in machine learning and scientific computing.

Probability Theory Dissertation Award

Michael Howes – for his important contributions to the efficient implementation and the mixing time of a certain collection of generalized hit-and-run Markov chains taking large steps.