Rate My Professor Tom Rainforth

Tom Rainforth is an Associate Professor of Statistical Machine Learning in the Department of Statistics at the University of Oxford, where he leads the RainML Research Lab. He also serves as a Tutorial Fellow at Mansfield College and is Principal Investigator on the European Research Council Starting Grant project UNICORN. Rainforth completed his DPhil in Machine Learning at the University of Oxford in 2017, supervised by Frank Wood and Michael A. Osborne. Prior to his doctorate, he obtained an integrated undergraduate and master's degree (MEng) in Mechanical Engineering from the University of Cambridge in 2013. Following graduation, he worked as a consultant for the Ferrari Formula 1 team, developing methodologies and software for car simulations. His early academic recognition includes the Sir George Nelson Prize in Applied Mechanics in 2013.

After his DPhil, Rainforth began as a postdoctoral researcher in the Oxford Department of Statistics. He advanced to Senior Research Fellow in Machine Learning, then Senior Researcher (Grade 9) from January to August 2024, before his appointment as Associate Professor in September 2024. Previously, he held the Florence Nightingale Bicentennial Fellowship. Rainforth's research specializes in statistical machine learning, with key interests in Bayesian experimental design, probabilistic programming, uncertainty quantification, and Bayesian statistics. His work has garnered over 5,499 citations according to Google Scholar. Prominent publications include 'Modern Bayesian Experimental Design' in Statistical Science (2024, co-authored with Adam Foster, Desi R. Ivanova, and Freddie Bickford Smith); 'Do Bayesian Neural Networks Need To Be Fully Stochastic?' presented at the International Conference on Artificial Intelligence and Statistics (AISTATS 2023, notable paper award); 'Tighter Variational Bounds are Not Necessarily Better' at the International Conference on Machine Learning (ICML 2018); and his doctoral thesis 'Automating Inference, Learning, and Design using Probabilistic Programming' (University of Oxford, 2017). He supervises approximately 10 DPhil students, delivers lectures in the department, and has given invited talks at events such as ML in PL.