I am a DPhil candidate in Machine Learning at the University of Oxford, supervised by Yarin Gal and Stephen Roberts. I am a member of the OATML group and the AIMS CDT. During my PhD, I was fortunate to have interned at NASA FDL-X (atmospheric density prediction from solar imagery) and Normal Computing (LLM agents for accelerating chip manufacturing).

I am excited about bringing machine learning to the forefront of scientific discovery. Methodologically, this includes topics such as data-efficient learning, differentiable simulations, and using LLMs to augment the scientific method (e.g. for hypothesis generation and experimental design). I also think about short/long term AI risks, and collaborate with the causal incentives working group.

Previously, I studied Physics at St John’s College, Cambridge, working on a machine learning approach for predicting the outcomes of material synthesis procedures in the Lee group. I then joined a ML Master’s at UCL, working with Humanloop to combat some of the issues with using active learning in practice. Outside of research, I’ve worked in venture capital, management consulting, and with a range of tech startups.

I am very happy to chat to anyone considering ML careers/PhDs - let me know if I can be of any help. Check my Notion page for some of the resources and advice I have found helpful.