Yee Whye Teh (main)

Yee Whye Teh became a lecturer (equivalent to assistant professor) at Gatsby Computational Neuroscience Unit, UCL, United Kingdom in 2007. He received his PhD from the University of Toronto in 2003 under the tutelage of Geoffrey Hinton. He was a postdoc at the University of California, Berkeley working with Michael Jordan, and at the National University of Singapore working with Wee Sun Lee, where he received a Lee Kwan Yew Postdoctoral Fellowship. He is interested in nonparametric Bayesian models, learning and inference in graphical models, and applications in information retrieval, natural language processing, computer vision, and computational biology. He has organized successful workshops on nonparametric Bayesian models at NIPS 2003 and NIPS 2005, as well as a workshop on Bayesian approaches in natural langauge processing at NIPS 2005. He is an area chair of NIPS 2007 and 2008, and senior program committee member of ICML 2007 and 2008.

Romain Thibaux

Romain Thibaux is a graduate student in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and is advised by Michael Jordan. His work on the Dirichlet and beta processes has established new theoretical links between Statistics techniques and machine learning problems, which have led to several new algorithms. He earned his Masters in 2003 from Stanford University.

Athanasios Kottas

Athanasios Kottas is Assistant Professor of Applied Mathematics and Statistics at University of California, Santa Cruz. He obtained his BSc and MSc in Mathematics from University of Ioannina, Greece, and his PhD in Statistics from University of Connecticut. His research interests include Bayesian nonparametric modeling and inference, analysis of computer model experiments, mixture models, quantile regression, spatial statistics, survival analysis, with applications in ecology and engineering.

Zoubin Ghahramani

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, UK, and is also Associate Research Professor of Machine Learning at Carnegie Mellon University. He obtained BA and BSE degrees from University of Pennsylvania, and a PhD in 1995 from MIT. He did a postdoc in Computer Science at University of Toronto working with Prof Geoff Hinton. His work has included research on human sensorimotor control, cognitive science, statistics, and machine learning. His current focus is on nonparametric Bayesian approaches to statistical machine learning. He has published over 100 peer reviewed papers, and serves on the editorial boards of several leading journals in the field, including JMLR, JAIR, IEEE PAMI, Annals of Statistics, Machine Learning, and Bayesian Analysis. He also serves on the Board of the International Machine Learning Society, and was Program Chair of the 2007 International Machine Learning Conference.

Michael Jordan

Michael Jordan is Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters from Arizona State University, and earned his PhD in 1985 from the University of California, San Diego. He was a professor at the Massachusetts Institute of Technology from 1988 to 1998. He has published over 250 research articles on topics in computer science, statistics, electrical engineering, molecular biology and cognitive neuroscience. His research in recent years has focused on probabilistic graphical models, kernel machines, nonparametric Bayesian methods and applications to problems in information retrieval, signal processing and bioinformatics, Prof. Jordan was named a Fellow of the American Association for the Advancement of Science (AAAS) in 2006. He is a Fellow of the IMS, a Fellow of the IEEE and a Fellow of the AAAI.

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