My research interests lie in the areas of machine learning and computer vision, including graphical models, efficient Markov chain Monte Carlo methods and variational inference methods for Bayesian models, deep learning and reinforcement learning.

My Ph.D. thesis is about a deterministic sampling algorithm known as herding. Herding takes as input a probabilistic distribution or a set of random samples, and outputs pseudo-samples without explicitly specifying a probabilistic model. These pseudo-samples are highly negatively correlated, and convey more information of the input distribution than i.i.d. samples of the same size. I have also worked on Bayesian inference for Markov random fields, as well as efficient and scalable MCMC methods.