In general, I am interested in the problems that limit ML systems to be taken out of the lab and used with confidence in the real world, motivated by practical applications in e.g. the sciences. This includes topics such as robustness, data-efficient/active learning, uncertainty quantification, human-AI collaboration and AI safety/ethics. I think bringing insights from causality theory into ML is a promising approach to address some of these problems.
Previously, I studied 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, 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.