EducationPhD, University of Oxford, 2010
MS, Washington University in St. Louis, 2004
AB, Washington University in St. Louis, 2004
Researches Bayesian machine learning for automating scientific discovery
Professor Garnett's main research interest is developing new Bayesian machine-learning techniques for sequential decision making under uncertainty. He is particularly interested in active learning—especially with atypical objectives—Bayesian optimization, intelligent approaches to approximate Bayesian inference, and Bayesian quadrature. He is also interested in learning problems involving large-scale graph data.
Professor Garnett came to WashU in January 2015.