I’m interested in developing interpretable machine learning models which complement human expertise, rather than attempt to replace it. For a high-level overview on some recent work, here’s a video of a talk I gave at the PROBPROG conference in Boston in September, 2018.

Previously I was a Turing research fellow based at the Alan Turing Institute; before that I was a DPhil student at University of Oxford, working with Frank Wood. My thesis focused on sequential Monte Carlo methods, and their application to general-purpose inference in probabilistic programs. A cliff-notes version is available as this short talk from back when I had long hair.