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

Teh, Y., Newman, D. and Welling, M. (2006) “A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation”, in NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems, pp. 1353–1360.
Teh, Y., Seeger, M. and Jordan, M. (2005) “Semiparametric latent factor models”, AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pp. 333–340.
Welling, M., Minka, T. and Teh, Y. (2005) “Structured region graphs: Morphing EP into GBP”, Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pp. 609–616.
Teh, Y. et al. (2005) “Sharing clusters among related groups: Hierarchical dirichlet processes”, in Advances in Neural Information Processing Systems.
Welling, M., Rosen-Zvi, M. and Teh, Y. (2004) “Approximate inference by Markov chains on union spaces”, Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, pp. 847–854.
Teh, Y. et al. (2004) “Energy-based models for sparse overcomplete representations”, Journal of Machine Learning Research, 4(7-8), pp. 1235–1260.
Welling, M. and Teh, Y. (2004) “Linear response for approximate inference”, in Advances in Neural Information Processing Systems.