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Michael  I  Jordan
TitleProfessor
InstitutionUniversity of California
Homepagehttp://www.cs.berkeley.edu/~jordan/
BioMichael Jordan is the Pehong Chen Distinguished 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 MIT from 1988 to 1998. He has published over 300 articles in statistics, electrical engineering, computer science, statistical genetics, computational biology and cognitive science. His research in recent years has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in computational biology, information retrieval, signal processing and speech recognition. Prof. Jordan was named to the National Academy of Sciences (NAS) in 2010 and the National Academy of Engineering (NAE) in 2010. He is a Fellow of the American Association for the Advancement of Science (AAAS). He was named a Neyman Lecturer of the Institute of Mathematical Statistics (IMS) for 2011 and was an IMS Medallion Lecturer in 2004. He is a Fellow of the IMS, a Fellow of the IEEE, a Fellow of the AAAI and a Fellow of the ASA.
NIPS Events*
NIPS 2010Invited TalkStatistical Inference of Protein Structure and Function
NIPS 2009WorkshopNonparametric Bayes
NIPS 2009PosterSharing Features among Dynamical Systems with Beta Processes
NIPS 2009OralSharing Features among Dynamical Systems with Beta Processes
NIPS 2008OralShared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
NIPS 2008PosterShared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
NIPS 2008PosterNonparametric Bayesian Learning of Switching Linear Dynamical Systems
NIPS 2008SpotlightNonparametric Bayesian Learning of Switching Linear Dynamical Systems
NIPS 2008PosterPosterior Consistency of the Silverman g-prior in Bayesian Model Choice
NIPS 2008PosterDiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
NIPS 2008SpotlightPosterior Consistency of the Silverman g-prior in Bayesian Model Choice
NIPS 2008PosterSpectral Clustering with Perturbed Data
NIPS 2008PosterEfficient Inference in Phylogenetic InDel Trees
NIPS 2008SpotlightEfficient Inference in Phylogenetic InDel Trees
NIPS 2008SpotlightSpectral Clustering with Perturbed Data
NIPS 2007PosterAgreement-Based Learning
NIPS 2007SpotlightAgreement-Based Learning
NIPS 2007SpotlightResampling Methods for Protein Structure Prediction with Rosetta
NIPS 2007SpotlightEstimating divergence functionals and the likelihood ratio by penalized convex risk minimization
NIPS 2007PosterEstimating divergence functionals and the likelihood ratio by penalized convex risk minimization
NIPS 2007PosterResampling Methods for Protein Structure Prediction with Rosetta
NIPS 2006PosterDistributed PCA and Network Anomaly Detection

*Since 2006