Biography: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 | {{{1}}} |
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]
He has been a research scientist at Google DeepMind.[11]
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.[12] 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
- ↑ 1.0 1.1 {{Google Scholar id}} template missing ID and not present in Wikidata.
- ↑ 2.0 2.1 "Yee-Whye Teh, Professor of Statistical Machine Learning". https://www.stats.ox.ac.uk/people/yee-whye-teh.
- ↑ 3.0 3.1 Yee Whye Teh at the Mathematics Genealogy Project
- ↑ 4.0 4.1 {{{1}}}
- ↑ 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.
- ↑ 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.
- ↑ {{DBLP}} template missing ID and not present in Wikidata.
- ↑ 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.
- ↑ , Wikidata Q33996665
- ↑ , Wikidata Q77688418
- ↑ Sample, Ian (2017-11-02). "Big tech firms' AI hiring frenzy leads to brain drain at UK universities" (in en-GB). The Guardian. ISSN 0261-3077. https://www.theguardian.com/science/2017/nov/02/big-tech-firms-google-ai-hiring-frenzy-brain-drain-uk-universities.
- ↑ "On Bayesian Deep Learning and Deep Bayesian Learning". https://nips.cc/Conferences/2017/Schedule?showEvent=8726.
 |  |
