University of Oxford, Department of Statistics logo
Professor Tom Rainforth
Professor of Statistical Machine Learning

Biographical sketch

I am Professor of Statistical Machine Learning, leader of the RainML Research Lab (rainml.uk), Tutorial Fellow at Jesus 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).   

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–Dec 2019), a Florence Nightingale Bicentennial Fellow and Tutor in Statistics and Probability (Jan 2020–Feb 2024), a Senior Research Fellow supported by my ERC grant (Mar 2024–Aug 2024), an Associate Professor (Sep 2024–Aug 2026), and finally (full) Professor of Statistical Machine Learning (Sep 2026– ).  

I originally studied Mechanical Engineering (MEng) at the University of Cambridge, while I did my DPhil in Oxford under the supervision of Frank Wood and Maike Osborne. 

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

Research Interests

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

  • Bayesian experimental design
  • Active learning
  • Probabilistic machine learning
  • Uncertainty quantification
  • Large language models

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

Publications

Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of Machine Learning Research, pp. 1676–1685.
Kossen, J. et al. (2022) “Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation”, in Advances in Neural Information Processing Systems.
Barrett, B. et al. (2022) “Certifiably Robust Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 3663–3683.
Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in Iclr 2022 10th International Conference on Learning Representations.
Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 1676–1685.
Campbell, A. et al. (2021) “Online Variational Filtering and Parameter Learning”, arXiv.
Joy, T. et al. (2021) “Capturing label characteristics in VAEs”, in Proceedings of the International Conference on Learning Representations (ICLR 2020). OpenReview.
Camuto, A. et al. (2021) “Towards a theoretical understanding of the robustness of variational autoencoders”, in. Journal of Machine Learning Research, pp. 3565–3573.
Willetts, M. et al. (2021) “IMPROVING VAES’ ROBUSTNESS TO ADVERSARIAL ATTACK”, in ICLR 2021 - 9th International Conference on Learning Representations.
Rudner, T. et al. (2021) “On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes”, in Proceedings of Machine Learning Research, pp. 9148–9156.

Contact Details

Email: rainforth@stats.ox.ac.uk

Office: 1.21

Pronouns: He/Him

Postdocs

Emily Alger
Freddie Bickford Smith
Gavin Kerrigan
Sahel Iqbal

Graduate Students

Adhi Saravanan
Alex Forster
Angus Phillips
Benjamin Chang
Deepro Choudhury
Guneet Singh Dhillon
Kianoosh Ashouritaklimi
Ole Jorgensen
Marcel Hedman
Rik Knowles
Tom Rossa
Zhuoyue Huang