Tomer Ullman is a cognitive scientist interested in common-sense reasoning, and building computational models for explaining high-level cognitive processes and the acquisition of new knowledge by children and adults. In particular, he is focused on how children and adults come to form intuitive theories of agents and objects, and providing both a functional and algorithmic account of how these theories are learned. Such an account would go a long way towards explaining the basics cogs and springs of human intelligence, and support the building of more human-like artificial intelligence. Dr. Ullman received in B.Sc in Cognitive Science and Physics from Hebrew University in 2008, and his Ph.D. in Brain and Cognitive Sciences from MIT in 2015. From 2015-2018 he was a post-doctoral associate at the Center for Brains, Minds, and Machines.

Dr. Ullman's research has been funded by the National Science Foundation, and the Center for Brains, Minds, and Machines.

Research interests: computational cognitive models; child development; judgement and decision making; hierarchical reasoning.