Joshua Tenenbaum is Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences, the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds and Machines (CBMM). His long-term goal is to reverse-engineer intelligence in the human mind and brain, and use these insights to engineer more human-like machine intelligence. In cognitive science, he is best known for developing theories of cognition as probabilistic inference in structured generative models, and applications to concept learning, causal reasoning, language acquisition, visual perception, intuitive physics, and theory of mind. In AI, he and his group have developed widely used models for nonlinear dimensionality reduction, probabilistic programming, and Bayesian unsupervised learning and structure discovery. His current research focuses on the development of common sense in children and machines, the neural basis of common sense, and models of learning as Bayesian program synthesis.
Joshua Tenenbaum received his PhD from MIT in 1999, and taught at Stanford from 1999 to 2002. He and his students’ research papers have been recognized with many awards at conferences in Cognitive Science, Computer Vision, Neural Information Processing Systems, Reinforcement Learning and Decision Making, and Robotics. He is the recipient of the Distinguished Scientific Award for Early Career Contributions in Psychology from the American Psychological Association (2008), the Troland Research Award from the National Academy of Sciences (2011), the Howard Crosby Warren Medal from the Society of Experimental Psychologists (2016), the R&D Magazine Innovator of the Year award (2018), and a MacArthur Fellowship (2019). Joshua Tenenbaum is a fellow of the Cognitive Science Society, the Society for Experimental Psychologists, and a member of the American Academy of Arts and Sciences.