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 [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]}

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]

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. ^[11] He served as program co-chair of the International Conference on Machine Learning (ICML) in 2017, one of the premier conferences in machine learning. ^[4]

References

1. Yee Whye Teh publications indexed by Google Scholar
2. "Yee-Whye Teh, Professor of Statistical Machine Learning". stats.ox.ac.uk.
3. Yee Whye Teh at the Mathematics Genealogy Project
4. www.stats.ox.ac.uk/~teh/
5. Gram-Hansen, Bradley (2021). Extending probabilistic programming systems and applying them to real-world simulators. ox.ac.uk (DPhil thesis). University of Oxford. OCLC   1263818188. EThOS   uk.bl.ethos.833365.
6. Gasthaus, Jan Alexander (2020). Hierarchical Bayesian nonparametric models for power-law sequences. ucl.ac.uk (PhD thesis). University College London. OCLC   1197757196. EThOS   uk.bl.ethos.807804.
7. Yee Whye Teh at DBLP Bibliography Server
8. Whye Teh, Yee (2003). Bethe free energy and contrastive divergence approximations for undirected graphical models. utoronto.ca (PhD thesis). University of Toronto. hdl:1807/122253. OCLC   56683361. ProQuest   305242430.
9. Geoffrey E. Hinton; Simon Osindero; Yee-Whye Teh (1 July 2006). "A fast learning algorithm for deep belief nets". Neural Computation . 18 (7): 1527–1554. doi:10.1162/NECO.2006.18.7.1527. ISSN   0899-7667. PMID   16764513. Zbl   1106.68094. Wikidata   Q33996665.
10. Yee W. Teh; Michael I. Jordan; Matthew J. Beal; David M. Blei (2005). "Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes" (PDF). Advances in Neural Information Processing Systems 17. Advances in Neural Information Processing Systems. Wikidata   Q77688418.
11. "On Bayesian Deep Learning and Deep Bayesian Learning". nips.cc.