Professor Tom Rainforth

Associate Professor of Statistical Machine Learning

Biographical sketch

I am an Associate Professor of Statistical Machine Learning, leader of the RainML Research Lab (rainml.uk), Tutorial Fellow at Mansfield College, and Principal Investigator of the ERC Starting Grant Data-Driven Algorithms for Data Acquisition (Mar 2024 - Feb 2029, funded by the UKRI Horizon Guarantee Scheme). Though I only started my current Associate Professor role in September 2024, I have been a member of the Department since 2017, first as a postdoc working with Yee Whye Teh (Sep 2017 - Aug 2019), then as an associate member as part of a Junior Research Fellow in Computer Science at Christ Church College (Sep 2019 - Dec 2019), then as a Florence Nightingale Bicentennial Fellow and Tutor in Statistics and Probability (Jan 2020 - Feb 2024), and finally a Senior Research Fellow supported by my own ERC grant (Mar 2024 - Aug 2024). I originally studied Mechanical Engineering (MEng) at the University of Cambridge, while I did my D.Phil in Oxford under the supervision of Frank Wood and Michael Osborne, focusing mostly on probabilistic programming and Monte Carlo methods. I also had a stint working in the Ferrari Formula 1 team in between the two.

Personal website: https://www.robots.ox.ac.uk/~twgr/

Research Interests

My research covers a wide range of topics in and around statistical machine learning and experimental design, with areas of particular interest including:

Bayesian experimental design
Probabilistic and data-efficient approaches to machine learning
Active learning
Deep learning, with a particular focus on probabilistic approaches, deep representation learning, and deep generative models
Probabilistic programming
Approximate inference and Monte Carlo methods

Please see my Google Scholar page for an up-to-date list of publications.

Publications

Xu, J. et al. (2021) “Group Equivariant Subsampling”, in Advances in Neural Information Processing Systems, pp. 5934–5946.

Fitzsimons, J. et al. (2020) “A note on blind contact tracing at scale with applications to the COVID-19 pandemic”, in. Association for Computing Machinery (ACM), pp. 1–6.

Zhou, Y. et al. (2020) “Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support”, in ICML 2020. ICML Proceedings.

Foster, A. et al. (2020) “A unified stochastic gradient approach to designing Bayesian-optimal experiments”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. PMLR, pp. 2959–2969.

Rainforth, T. et al. (2020) “Target–aware Bayesian inference: how to beat optimal conventional estimators”, Journal of Machine Learning Research, 21(88), p. 1−54.

Zhou, Y. et al. (2020) “Divide, conquer, and combine: A new inference strategy for Probabilistic Programs with Stochastic Support”, 37th International Conference on Machine Learning, ICML 2020, PartF168147-15, pp. 11471–11482.

Foster, A. et al. (2019) “Variational Bayesian optimal experimental design”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Conference on Neural Information Processing Systems.

Foster, A. et al. (2019) “Variational Bayesian Optimal Experimental Design”, in.

Locatello, F. et al. (2019) “On the fairness of disentangled representations”, in Advances in Neural Information Processing Systems. NeurIPS.

Goliński, A., Wood, F. and Rainforth, T. (2019) “Amortized Monte Carlo integration”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 2309–2318.

Contact Details

Email: rainforth@stats.ox.ac.uk

Office: 1.21

Pronouns: He/Him

Research Groups

Computational Statistics and Machine Learning

Graduate Students

Alex Forster
Angus Phillips
Freddie Bickford Smith
Guneet Singh Dhillon
Andrew Campbell
Desi Ivanova
Jannik Kossen
Kianoosh Ashouritaklimi

Ning Miao

Marcel Hedman
Tim Reichelt
Mrinank Sharma
Yuyang Shi
Shahine Bouabid
Jin Xu