Yee Whye Teh

Yee-Whye Teh
Alma materUniversity of Waterloo (BMath)
University of Toronto (PhD)
Known forHierarchical Dirichlet process
Deep belief networks
Scientific career
FieldsMachine learning
Artificial intelligence
Statistics
Computer science[1]
InstitutionsUniversity of Oxford
DeepMind
University College London
University of California, Berkeley
National University of Singapore[2]
ThesisBethe free energy and contrastive divergence approximations for undirected graphical models (2003)
Doctoral advisorGeoffrey Hinton[3]
Websitewww.stats.ox.ac.uk/~teh/ Edit this at Wikidata

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]

  1. ^ a b Yee Whye Teh publications indexed by Google Scholar Edit this at Wikidata
  2. ^ Cite error: The named reference sing was invoked but never defined (see the help page).
  3. ^ Cite error: The named reference mathgene was invoked but never defined (see the help page).
  4. ^ www.stats.ox.ac.uk/~teh/ Edit this at Wikidata
  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. Free access icon
  7. ^ Yee Whye Teh at DBLP Bibliography Server Edit this at Wikidata