I’m a Florence Nightingale Bicentennial Fellow at the Department of Statistics, University of Oxford. Prior to that I was a graduate student on the StatML CDT programme at the University of Oxford, working with Tom Rainforth and Yee Whye Teh.

During my PhD I’ve interned as a Research Scientist at Microsoft Research Cambridge, where I focused on causal machine learning, and at Meta AI (FAIR Labs) NYC, where I worked on neural data compression. Before StatML, I spent four years in quant finance – first in quantitative equity research at UBS and later in cross-asset systematic trading strategies structuring at Goldman Sachs.

I'm broadly interested in probabilistic machine learning. I have worked on Bayesian experimental design, causality and neural data compression. Nowadays I'm mostly interested in robust evaluations of language models ("LLM Evals") and uncertainty quantification for LLMs.

I occasionally blot at Probably Approximately Incorrect.