Yee Whye Teh
Yee-Whye Teh | |
---|---|
Alma mater | |
Thesis | Bethe free energy and contrastive divergence approximations for undirected graphical models (2003) |
Doctoral advisor | Geoffrey Hinton[3] |
Website | www |
Yee-Whye Teh is a professor of
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
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
- ^ a b Yee Whye Teh publications indexed by Google Scholar
- ^ a b "Yee-Whye Teh, Professor of Statistical Machine Learning". stats.ox.ac.uk.
- ^ a b Yee Whye Teh at the Mathematics Genealogy Project
- ^ a b www
.stats .ox .ac .uk /~teh / - EThOS uk.bl.ethos.833365.
- EThOS uk.bl.ethos.807804.
- ^ Yee Whye Teh at DBLP Bibliography Server
- ProQuest 305242430.
- Wikidata Q33996665.
- Wikidata Q77688418.
- ^ "On Bayesian Deep Learning and Deep Bayesian Learning". nips.cc.