I'm currently working in the Department as a Florence Nightingale Bicentennial Fellow. I previously completed a postdoc with Chris Holmes and Arnaud Doucet, working on causal inference and conformal prediction. Before that I completed my DPhil as part of the AIMS CDT under the supervision of Arnaud Doucet and George Deligiannidis. My thesis covered several topics in (primarily Bayesian) computational statistics and machine learning, including Monte Carlo methods and deep generative modelling. Before they left Oxford, I also worked with Frank Wood and Hongseok Yang on probabilistic programming.

I am broadly interested in ensuring the robustness of complex, safety-critical systems. I have worked on techniques for reliable uncertainty quantification, causal inference, as well as a variety of topics across machine learning. An underlying focus of my research is on methodology that is valid under minimal assumptions, which allows their application in large-scale real-world settings where more specific assumptions may be difficult to justify.