I am currently a research associate at the University of Cambridge and a visiting researcher at The Alan Turing Institute, where I am affiliated to the data-centric engineering programme. From August 2019 onwards, I will be moving to University College London where I will be taking up a position of Lecturer in Statistical Science (equivalent to Assistant Professor).
Prior to Cambridge, I was a research assistant in the Department of Mathematics at Imperial College London, and before that, a PhD student on the joint centre for doctoral training in Statistical Science led by the universities of Warwick and Oxford.
My current research interests are at the interface of computational statistics, machine learning and applied mathematics. In particular, I work on methodology for statistical computation and inference for large scale and computationally expensive probabilistic models. To do so, I use a variety of tools including kernel methods, Monte Carlo methods, stochastic process theory and Stein’s method.
For more details on my research, see my publications page or my Google Scholar profile. Alternatively, you can catch me at one of my presentations. For supervision opportunities, see also this page.
News
I have two new papers on doing inference for intractable model. The first paper, called Minimum Stein Discrepancy Estimators, is a very flexible framework to do inference for models with unnormalised likelihoods which is grounded in Stein’s method and can recover many existing methods, such as score-matching and contrastive divergence, as special cases. The second, called Statistical Inference for Generative Models with Maximum Mean Discrepancy considers the problem of inference for generative models, and studies the flexibility afforded by the choice of reproducing kernel.
New paper on approximating expensive and multimodal probability measures: Stein Point Markov Chain Monte Carlo. This paper significantly improves the scalability of the Stein Point algorithms, which were proposed as an alternative to MCMC methods for Bayesian computation for expensive posterior distributions. The paper will appear in the proceedings of ICML 2019.
Our paper on “Probabilistic Integration: A Role in Statistical Computation?” has been accepted for publication in Statistical Science and will appear together with invited discussions from leading researchers in statistics, as well as a rejoinder. There are three discussion pieces by (i) Fred Hickernel and R. Jagadeeswaran, (ii) Art Owen, and (iii) Michael Stein and Ying Hung.