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

Cai, J., Lee, W. and Teh, Y. (2007) “Improving word sense disambiguation using topic features”, EMNLP-CoNLL 2007 - Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1015–1023.
Teh, Y., Newman, D. and Welling, M. (2007) “A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation”, Advances in Neural Information Processing Systems, pp. 1353–1360.
Kurihara, K., Welling, M. and Teh, Y. (2007) “Collapsed variational dirichlet process mixture models”, in IJCAI International Joint Conference on Artificial Intelligence, pp. 2796–2801.
Teh, Y., Görür, D. and Ghahramani, Z. (2007) “Stick-breaking construction for the Indian buffet process”, Journal of Machine Learning Research, 2, pp. 556–563.
Cai, J., Lee, W. and Teh, Y. (2007) “NUS-ML: Improving word sense disambiguation using topic features”, in ACL 2007 - SemEval 2007 - Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 249–252.
Xingt, E. et al. (2006) “Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture”, ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, 2006, pp. 1049–1056.