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Professor Yee Whye Teh
Professor of Statistical Machine Learning

Biographical Sketch

Prior to joining Oxford, I was a Lecturer then Reader of Computational Statistics and Machine Learning at the Gatsby Neuroscience Unit, UCL from 2007 to 2012. I obtained my PhD in Computer Science at the University of Toronto in 2003. This was followed by two years as a postdoctoral fellow at University of California, Berkeley, then as Lee Kuan Yew Postdoctoral Fellow at the National University of Singapore.

Research Interests

My research interests lie in the general areas of machine learning, Bayesian statistics and computational statistics. Although my group works on a variety of topics ranging from theoretical, through to methodological and applications, I am personally particularly interested in three (overlapping) themes: Bayesian nonparametrics and probabilistic learning, large scale machine learning, and deep learning.

These themes are motivated by the phenomenal growth in the quantity, diversity and heterogeneity of data now available. The analysis of such data is crucial to opening doors to new scientific frontiers and future economic growth. In the longer term, the development of general methods that can deal with such data are important testing grounds for artificial general intelligence systems.

Publications

Subedi, A. et al. (2026) “Steering Diffusion Models to Rare Events with Sequential Monte Carlo.”
Mammadov, A. et al. (2026) “WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps.”
Mammadov, A. et al. (2026) “Exact Posterior Score Estimation for Solving Linear Inverse Problems.”
Lam, K. et al. (2026) “Metropolis-Adjusted Diffusion Models”, arXiv.
Mammadov, A. et al. (2026) “Variational Flow Maps: Make Some Noise for One-Step Conditional Generation.”
Nguyen-Hien, T. et al. (2025) “Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs.”
Lu, X., Rainforth, T. and Teh, Y. (2023) “Daisee: Adaptive importance sampling by balancing exploration and exploitation”, Scandinavian Journal of Statistics, 50(3), pp. 1298–1324.
Klarner, L. et al. (2023) “Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions”, arXiv.
Xu, J. et al. (2023) “Deep Stochastic Processes via Functional Markov Transition Operators.”
De Bortoli, V. et al. (2023) “Riemannian score-based generative modelling”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 2406–2422.