My research interests are in scalable Markov chain Monte Carlo (MCMC) algorithms. A common problem when fitting complex models is overfitting, and Bayesian methods provide a natural solution to this. But MCMC, one of the most popular methods for fitting Bayesian methods, does not scale well with the dataset size. As dataset sizes have been increasing, the need to improve the scalability of MCMC has become clear.
My PhD began with a comparison of current methods under different scenarios, to work out where the methods could be improved. More recently I have been working on using control variates to improve the efficiency of Stochastic gradient MCMC, a particular class of scalable MCMC algorithm. I am now working on developing scalable MCMC methods for Bayesian nonparametrics, an important class of problems with applications in genetics, topic modelling and networks.