In general, I am interested in the problems that currently limit ML systems to be taken out of the lab and used with confidence in the real world, in application areas such as the sciences and healthcare. This includes topics such as out-of-distribution robustness, data-efficient learning, and AI safety/ethics. I think bridging causality theory with representation learning is a promising approach to address some of these problems.
I did my undergrad in Physics at St John’s College, Cambridge, where for my thesis I worked 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, alongside working at AI startups, I have worked in management consulting, venture capital, and have won prizes at several competitive hackathons across the world.
I am always 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 have found helpful over the years.