Yee-Whye TEH

Yee-Whye Teh is Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Director at Google DeepMind working on AI research and leading the Data-Efficient and Bayesian Learning team.

He obtained his Ph.D. at the University of Toronto (under Prof. Geoffrey E. Hinton), and did postdoctoral work at the University of California at Berkeley (under Prof. Michael I. Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). He was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL during 2007-2012. He was a European Research Council Consolidator Fellow, and a Tutorial Fellow at University College, Oxford. He is a fellow of the ELLIS Society, where he co-directs the ELLIS Programme in Robust Machine Learning and the Oxford ELLIS Unit. His research interests are in machine learning, in particular deep learning, probabilistic machine learning and Bayesian nonparametrics. He develops novel models and efficient algorithms for inference and learning. He gave the Breiman Lecture at NeurIPS 2017, the IMS Medallion Lecture at JSM 2019, and keynotes at MLSP 2017, KDD 2018, UAI 2019, and NordStat 2020 (delayed to 2021).

He was programme co-chair (with Prof. Michael Titterington) of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, programme co-chair (with Prof Doina Precup) of the International Conference on Machine Learning (ICML) 2017, and was an associate editor for Bayesian Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Machine Learning Journal, Statistical Sciences, Journal of the Royal Statistical Society Series B and Journal of Machine Learning Research.