She obtained her PhD from the Computational and Biological Learning group at the University of Cambridge, and spent two years as a post doc in the SAILING laboratory at Carnegie Mellon University. She is interested in nonparametric Bayesian methods for use in machine learning applications. The nonparametric Bayesian paradigm is an elegant and flexible approach for modeling complex data of unknown latent dimensionality. In particular, she is interested in dependent nonparametric processes — distributions over collections of measures indexed by values in some covariate space. Such models are appropriate for spatio-temporally variable data, and for sharing information between related tasks. She is also interested in the development of fast and scalable inference algorithms for Bayesian nonparametric models.