Josh Tenenbaum is a professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He studies learning, reasoning and perception in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities.
He and his collaborators have pioneered accounts of human intelligence based on inference in sophisticated probabilistic models. His current work focuses on understanding how people come to be able to learn new concepts from very sparse data—how we “learn to learn”—and on characterizing the nature and origins of people’s intuitive theories about the physical and social worlds.
Tenenbaum received his Ph.D. from MIT in 1999, and was a member of the Stanford University faculty in Psychology and (by courtesy) Computer Science from 1999 to 2002. 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), the Distinguished Scientific Award for Early Career Contribution to Psychology from the American Psychological Association (2008), and the Troland Research Award from the National Academy of Sciences (2011).
Photo Credit: Donna Coveney