Banner
Conference Videos | Workshop Videos | Overview | Program Highlights | Tutorials | Conference Sessions | Workshops | Publication Models Debate | Demonstrations | Mini Symposia | Accepted Papers | Dates | Committees
Josh  Tenenbaum
InstitutionMassachusetts Institute of Technology
BioJosh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).
NIPS Events*
NIPS 2010WorkshopTransfer Learning by Learning Rich Generative Models.
NIPS 2010Invited TalkHow to Grow a Mind: Statistics, Structure and Abstraction
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