Yee Whye Teh

Yee-Whye Teh
Alma mater	University of Waterloo (BMath) University of Toronto (PhD)
Known for	Hierarchical Dirichlet process Deep belief networks
Scientific career
Fields	Machine learning Artificial intelligence Statistics Computer science
Institutions	University of Oxford DeepMind University College London University of California, Berkeley National University of Singapore
Thesis	Bethe free energy and contrastive divergence approximations for undirected graphical models (2003)
Doctoral advisor	Geoffrey Hinton

Yee-Whye Teh is a professor of statistical machine learning in the Department of Statistics, University of Oxford. Prior to 2012 he was a reader at the Gatsby Charitable Foundation computational neuroscience unit at University College London. His work is primarily in machine learning, artificial intelligence, statistics and computer science.

Education

Teh was educated at the University of Waterloo and the University of Toronto where he was awarded a PhD in 2003 for research supervised by Geoffrey Hinton.

Research and career

Teh was a postdoctoral fellow at the University of California, Berkeley and the National University of Singapore before he joined University College London as a lecturer.

Teh was one of the original developers of deep belief networks and of hierarchical Dirichlet processes.

He has been a research scientist at Google DeepMind.

Awards and honours

Teh was a keynote speaker at Uncertainty in Artificial Intelligence (UAI) 2019, and was invited to give the Breiman lecture at the Conference on Neural Information Processing Systems (NeurIPS) 2017. He served as program co-chair of the International Conference on Machine Learning (ICML) in 2017, one of the premier conferences in machine learning.