My research interests include the theory and applications of machine learning, Bayesian statistics, deep learning and data science. My theoretical interests expand across topics such as Gaussian processes, neural networks, Bayesian non-parametric models, probabilistic graphical models, mixture models and latent variable models, kernel methods, hidden Markov models, nonlinear dynamical systems and stochastic differential equations. I am particularly interested in developing new practical algorithms for solving intractable inference problems through the use of Markov chain Monte Carlo, variational inference and stochastic approximation techniques.