About

I am a theoretical physicist now working as a machine learning researcher. My primary interests are in machine learning theory, algorithmic foundations, and mathematically grounded or methodological approaches to machine learning for scientific discovery. Currently, as I move deeper into the field, I am perhaps most motivated by the question: how should we learn, infer, simulate, represent, and make decisions about complex scientific systems when data are limited, experiments are expensive, uncertainty matters, and mechanistic structure is available?

My research interests are therefore increasingly centred on the intersections between generative modelling, active learning, reinforcement learning, and sequential decision-making, as well as inverse problems and simulation-based inference, scientific machine learning (SciML), and representation learning for scientific data. I am also interested in emerging areas such as probabilistic numerical methods, especially where they connect numerical computation with uncertainty quantification, inference, and learning.

My current research focuses primarily on applications in computational biology and physics, although I also work more broadly on quantitative modelling and general applied research problems. You can read more about me here.