Sam Bright-Thonney

I am an IAIFI Fellow at MIT, where I work on problems at the intersection of AI and physics. Recently, I’ve been particularly interested in using AI to automate and accelerate research in the physical sciences, as well as mathematically principled approaches for developing new AI tools. You can find my CV here.

I’m also very drawn to recent literature on the theory and “phenomenology” of machine learning. The latter is a term I’ve borrowed/adapted from particle theory, and which broadly means: empirically-grounded work that seeks to understand, characterize, and build effective theories of complex model behavior without relying on intractable first principles calculations. From a fundamental physics perspective, AI is currently in an incredibly exciting and familiar place: “experimental” innovations (the deep learning revolution, LLMs, etc.) have advanced far beyond what our mathematical theories can explain. In the 20th century, an analogous situation in physics led to quantum chromodynamics, electroweak unification, and the development of the Standard Model. In this century, I think that understanding AI will be an equally fundamental and animating problem, and an essential one to make progress on if we are to use these tools responsibly.

Before joining IAIFI, I completed my PhD in physics at Cornell University, where I worked on the CMS experiment at CERN’s Large Hadron Collider. My thesis work included a first-of-its-kind analysis looking for dijet resonances using machine learning, and a pair of challenging searches for inelastic dark matter in final states with soft/displaced leptons.

I’m always happy to chat with new people and find new collaborators – feel free to reach out!