David B. Dunson


Arts and Sciences Distinguished Professor of Statistical Science

My research focuses on the development of statistical and machine learning methodology motivated by complex and high dimensional data. Primary motivating applications include ecology and biodiversity, neuroscience, environmental health, epidemiology and genomics. A particular emphasis is on developing flexible Bayesian and probabilistic learning approaches for inferring and exploiting lower dimensional structure in high-dimensional and structured data.  This “structure” may take the form of a network, a high dimensional table or array, functions/surfaces, molecular structure, trees, etc.  A general emphasis is on developing novel modeling frameworks and corresponding algorithms for posterior computation, and more broadly uncertainty quantification, using Bayes and generalized Bayes frameworks for inference.  Broad new methodological frameworks are developed concretely motivated by important applied problems.