I am a member of the Machine Intelligence and Perception group at Microsoft Research Cambridge.

My main general research interest is Bayesian inference for simulation-based or mechanistic models.  In the past this has included algorithm development for approximate Bayesian computation (ABC), a framework for performing Bayesian inference in the likelihood-free setting.

I am also interested in machine learning models and inference techniques applied to computational biology, specifically cancer genomics using data from the cancer genome atlas (TCGA).

In the past I worked on models and algorithms for Bayesian non-parametrics, during my PhD at the University of Toronto.