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.
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.
Prior to UCL, I was a PhD student on the joint centre for doctoral training between the Departments of Statistics at Warwick and Oxford. I then spent a year as a postdoctoral researcher, first in the Department of Mathematics at Imperial College London, then in the Department of Engineering at the University of Cambridge.
For more details, see my Publications page or my Google Scholar profile. Alternatively, you can catch me at one of my Presentations. If you are a student/postdoc interested in working with me, see also this page.
I will be taking part in the Newton Institute programme on The mathematical and statistical foundation of future data-driven engineering at the University of Cambridge from January-June 2023.
Our paper on “Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap” received the Best Paper Award at AISTATS 2022!
Zhuo Sun and Takuo Matsubara each received a Student Paper Award from the section on Bayesian Statistical Science of the American Statistical Association for our papers on “Vector-valued control variates” and “Robust generalised Bayesian inference for intractable likelihoods”.