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.
