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. and Roweis, S. (2003) “Automatic alignment of local representations”, in Advances in Neural Information Processing Systems.
Teh, Y. and Roweis, S. (2002) “Automatic Alignment of Local Representations”, in NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems, pp. 841–848.
Teh, Y. and Welling, M. (2002) “The unified propagation and scaling algorithm”, in Advances in Neural Information Processing Systems.
Teh, Y. and Hinton, G. (2001) “Rate-coded restricted boltzmann machines for face recognition”, in Advances in Neural Information Processing Systems.
Hinton, G., Ghahramani, Z. and Teh, Y. (2000) “Learning to parse images”, in Advances in Neural Information Processing Systems, pp. 463–469.
Bacchus, F. and Teh, Y. (1998) “Making Forward Chaining Relevant”, in Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems, AIPS 1998.
Gorur, D. and Teh, Y. (no date) “An Efficient Sequential Monte-Carlo Algorithm for Coalescent Clustering”, in.
Kosiorek, A. et al. (no date) “Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects”, in.
Teh, Y., Elliott, L. and Blundell, C. (no date) “Bayesian Nonparametric Modelling of Genetic Variations using Fragmentation-Coagulation”, Journal of Machine Learning Research [Preprint].