About
Tom Rainforth is an Associate Professor of Statistical Machine Learning at the University of Oxford's Department of Statistics, leading the RainML Research Lab (rainml.uk). He holds a Tutorial Fellowship at Mansfield College and is Principal Investigator of the ERC Starting Grant 'Data-Driven Algorithms for Data Acquisition' (2024–2029). Previously, he held roles including a postdoc under Yee Whye Teh (2017–2019), Junior Research Fellow at Christ Church College (2019–2019), and Florence Nightingale Bicentennial Fellow (2020–2024). He earned his MEng in Mechanical Engineering from the University of Cambridge and his D.Phil from Oxford under Frank Wood and Michael Osborne, focusing on probabilistic programming and Monte Carlo methods. He briefly worked in Ferrari's Formula 1 team.
Research Interests: Bayesian experimental design, probabilistic and data-efficient machine learning, active learning, deep learning (with a focus on probabilistic approaches), probabilistic programming, and Monte Carlo methods. His work emphasizes statistical efficiency and adaptive algorithms.
Publications: Recent contributions span modern Bayesian experimental design, adaptive importance sampling (Daisee), and applications of probabilistic methods in LLMs and generative models. His research bridges theory and practice, addressing challenges in scalability and robustness.
Awards: ERC Starting Grant (2024–2029), highlighting his leadership in foundational AI research.
Advising & Grants: Supervises 15 graduate students, including work on Bayesian neural networks, generative flows, and experimental design. His ERC grant supports cutting-edge data-driven algorithm development.
Labs & Teams: Directs the RainML Lab, which develops scalable Bayesian methods and probabilistic AI systems.
