His lab works on problems at the intersection of neuroscience, statistics, and machine learning. They develop statistical methods for characterizing structure in high dimensional data, and collaborate very closely with experimental groups to study neural systems and the computations they perform. His lab is also interested in the brain's ability to perform statistical inference in naturalistic tasks and in the theoretical principles governing the function and design of sensory systems. Current research topics include sensory-motor decision making, visual perception, adaptation, scalable methods for high-dimensional regression and classification problems, latent variable models, functional connectivity, statistical inference for detailed biophysical models, information theory, and nonparametric Bayesian statistics.