Josh Tenenbaum

Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology

Email: jbt AT mit DOT edu
Phone: 617-452-2010 (office), 617-253-8335 (fax)
Mail: Building 46-4015, 77 Massachusetts Avenue, Cambridge, MA 02139
Curriculum Vitae (as of October 2017)

If you are an undergraduate at MIT interested in research with our group, please send a short email about your interests and background (as specific as possible), with resume/CV and any previous research papers, to Thanks!

If you are applying to work with our group via an MIT PhD program, please consider applying to both the Department of Brain and Cognitive Sciences, and Electrical Engineering and Computer Science. I work with students through both programs. Students interested in computational cognitive modeling or cognitive AI are especially encouraged to apply to both, as they have different features and that will maximize your chances of acceptance. (Because I am not a primary EECS faculty member, you won't see my name on the list of prospective EECS PhD advisors but just mention me in your research statement and I will see your application.) I strongly encourage applications from students in groups underrepresented in cognitive science (especially computational cognitive science) or AI.

For all students, I am happy to recieve an email about your interests, but due to the volume of inquiries I receive, I cannot respond to most questions about PhD applications. Please don't take a lack of response as any lack of interest in your application! If you mention my name in your research statement, I will see your application, and I will make sure to keep an eye out for applications from any student who does email me.

Research interests

My colleagues and I in the Computational Cognitive Science group want to understand that most elusive aspect of human intelligence: our ability to learn so much about the world, so rapidly and flexibly. I like to ask, "How do we humans get so much from so little?" and by that I mean how do we acquire our commonsense understanding of the world given what is clearly by today's engineering standards so little data, so little time, and so little energy.

Consider how given just a few relevant experiences, even young children can infer the meaning of a new word, the hidden properties of an object or substance, or the existence of a new causal relation or social rule. These inferences go far beyond the data given: after seeing three or four examples of "horses", a two-year-old will confidently judge whether any new entity is a horse or not, and she will be mostly correct, except for the occasional donkey or camel.

We want to understand these everyday inductive leaps in computational terms. What is the underlying logic that supports reliable generalization from so little data? What are its cognitive and neural mechanisms, and how can we build more powerful learning machines based on the same principles?

These questions demand a multidisciplinary approach. Our group's research combines computational models (drawing chiefly on Bayesian statistics, probabilistic generative models, and probabilistic programming) with behavioral experiments in adults and children. Our models make strong quantitative predictions about behavior, but more importantly, they attempt to explain why cognition works, by viewing it as an approximation to ideal statistical inference given the structure of natural tasks and environments.

While our core interests are in human learning and reasoning, we also work actively in machine learning and artificial intelligence. These two programs are inseparable: bringing machine-learning algorithms closer to the capacities of human learning should lead to more powerful AI systems as well as more powerful theoretical paradigms for understanding human cognition.

Current research in our group explores the computational basis of many aspects of human cognition: learning concepts, judging similarity, inferring causal connections, forming perceptual representations, learning word meanings and syntactic principles in natural language, noticing coincidences and predicting the future, inferring the mental states of other people, and constructing intuitive theories of core domains, such as intuitive physics, psychology, biology, or social structure.

The Computational Cognitive Science Group

Up to date publications (courtesy of Google scholar)

Publications sorted by citation popularity

Publications sorted by recency

Representative reading and talks

Papers (listed chronologically - very out of date!)













1999 and before

Papers (by topic - this list is somewhat out of date)

Nonlinear dimensionality reduction

Separating style and content

Concept learning and generalization

Learning and Representing Word Meanings

Learning and similarity

Probabilistic reasoning

Causal learning and inference

Theory of mind

Lab mascots
Abigail "Avishka" Lea Tenenbaum