Arts & Sciences Distinguished Professor, Duke University
Carnegie Centenary Professor 2018, University of Edinburgh
Bayesian statistical and data science methods motivated by complex applications. Ongoing methodologic research focuses on nonparametric Bayes, latent structure learning, big data, scalable Bayesian inferences, machine learning, and high-dimensional low sample size problems. An emphasis is on approaches for learning low-dimensional structure underlying high-dimensional "objects" (images, surfaces, shapes, text, arrays, networks) with uncertainty quantification. This work involves inter-discplinary thinking at the intersection of statistics, mathematics and computer science. Motivation comes from applications in epidemiology,
environmental health, neurosciences, genetics, fertility and other settings (music, fine arts, humanities).