I am a Lecturer (equivalent to Assistant Professor) in the Department of Statistical Science at University College London. I am also a Group Leader at The Alan Turing Institute, the UK’s national institute for Data Science and AI, where I am affiliated to the Data-Centric Engineering programme. There, I lead research on the Fundamentals of Statistical Machine Learning with an emphasis on methodology for complex engineering models.
My research interests are at the interface of computational statistics, machine learning and applied mathematics. I work on methodology for statistical computation and inference for large scale and computationally expensive probabilistic models. In particular, I am interested in the development of algorithms for numerical integration and sampling, as well as methodology for inference with intractable models. In my research, I like to use a variety of tools including kernel methods, Monte Carlo methods, stochastic process theory and Stein’s method.
Prior to UCL, I was a PhD student on the joint centre for doctoral training between the Departments of Statistics at Warwick and Oxford, then spent a year first as research assistant in the Department of Mathematics at Imperial College London, then as a research associate in the Department of Engineering at the University of Cambridge.
My PhD thesis on “Statistical Computation with Kernels” received an Honorable Mention for the Savage Award in the section “Theory and Methodology”. This is awarded each year by the International Society on Bayesian Analysis (ISBA) for “a dissertation that makes important original contributions to the foundations, theoretical developments, and/or general methodology of Bayesian analysis”.
I have been awarded an Amazon Research Award by Amazon Science for a project on “Transfer Learning for Numerical Integration in Expensive Machine Learning Systems”.
I am now research group lead for Data Science Methodology for Weather and Climate, which is part of the new Met Office Academic Partnership with UCL.