I’m a Florence Nightingale Bicentennial Fellow at the Department of Statistics, University of Oxford (faculty fellowship). 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 Reseach 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, with a focus on robustness and uncertainty quantification. Currently, I’m thinking a lot about rigorous evaluation of LLMs and “foundation models” more generally. For more about my work, see my talks, publications or my blog—Probably Approximately Incorrect.