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 [1]
Institutions	University of Oxford DeepMind University College London University of California, Berkeley National University of Singapore [2]
Thesis	Bethe free energy and contrastive divergence approximations for undirected graphical models  (2003)
Doctoral advisor	Geoffrey Hinton [3]
Website	www.stats.ox.ac.uk/~teh/

Yee-Whye Teh is a professor of statistical machine learning in the Department of Statistics, University of Oxford. ^{[4] [5]} Prior to 2012 he was a reader at the Gatsby Charitable Foundation computational neuroscience unit at University College London. ^[6] His work is primarily in machine learning, artificial intelligence, statistics and computer science. ^{[1] [7]}

Contents

• Education
• Research and career
• Awards and honours
• References

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. ^{[3] [8]}

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. ^[2]

Teh was one of the original developers of deep belief networks ^[9] and of hierarchical Dirichlet processes. ^[10]

References