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Josh  Tenenbaum
InstitutionMassachusetts Institute of Technology
BioJosh Tenenbaum studies learning and reasoning in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing artificial intelligence closer to human-level capacities. He received his Ph.D. from MIT in 1999, and from 1999-2002, he was a member of the Stanford University faculty in the Departments of Psychology and (by courtesy) Computer Science. In 2002, he returned to MIT as an assistant professor in the Department of Brain and Cognitive Sciences. He currently holds the Paul E. Newton Career Development Chair at MIT, and is also a member of the Computer Science and Artificial Intelligence Laboratory. He has published over 60 papers in cognitive science, machine learning, and artificial intelligence. He serves as an Associate Editor of the journal Cognitive Science and has been active on the program committees of NIPS, the Annual Conference of the Cognitive Science Society, and various smaller workshops. In 2006 he received the New Investigator Award from the Society for Mathematical Psychology.
NIPS Events*
NIPS 2009WorkshopBounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain
NIPS 2009WorkshopAnalyzing Networks and Learning With Graphs
NIPS 2009PosterHelp or Hinder: Bayesian Models of Social Goal Inference
NIPS 2009PosterPerceptual Multistability as Markov Chain Monte Carlo Inference
NIPS 2009SpotlightPerceptual Multistability as Markov Chain Monte Carlo Inference
NIPS 2009PosterExplaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
NIPS 2009OralExplaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
NIPS 2009PosterModelling Relational Data using Bayesian Clustered Tensor Factorization
NIPS 2008WorkshopProbabilistic Programming: Universal Languages, Systems and Applications
NIPS 2008WorkshopMachine learning meets human learning
NIPS 2007WorkshopThe Grammar of Vision: Probabilistic Grammar-Based Models for Visual Scene Understanding and Object Categorization
NIPS 2007SpotlightA Bayesian Framework for Cross-Situational Word-Learning
NIPS 2007PosterA complexity measure for intuitive theories
NIPS 2007PosterA Bayesian Framework for Cross-Situational Word-Learning
NIPS 2006TalkLearning annotated hierarchies from relational data
NIPS 2006SpotlightMultiple timescales and uncertainty in motor adaptation
NIPS 2006TalkCombining causal and similarity-based reasoning
NIPS 2006TutorialBayesian Models of Human Learning and Inference
NIPS 2006PosterMultiple timescales and uncertainty in motor adaptation
NIPS 2006PosterLearning annotated hierarchies from relational data
NIPS 2006PosterCombining causal and similarity-based reasoning
NIPS 2006PosterCausal inference in sensorimotor integration

*Since 2006