How is it that biological systems can be so imprecise, so ad hoc, and so inefficient, yet accomplish (seemingly) simple tasks that still elude state-of-the-art artificial systems? In this context, I will introduce some of the themes central to CMU's new BrainHub Initiative by discussing: (1) The complexity and challenges of studying the mind and brain; (2) How the study of the mind and brain may benefit from considering contemporary artificial systems; (3) Why studying the mind and brain might be interesting (and possibly useful) to computer scientists.
Biography: Michael J. Tarr is the Head of the Department of Psychology in Carnegie Mellon Universitys Dietrich College of Humanities and Social Sciences and the Chair of Carnegie Mellon's BrainHub Steering Committee. He studies the neural, cognitive and computational mechanisms underlying visual perception and cognition. He is particularly interested in object and face recognition, how we become visual experts for non-face object domains, and how visual perception interacts with our other senses, with cognition, and with social and affective processing. Much of his work is predicated on the idea that models of artificial and biological vision have something (meaningful) in common and that both disciplines will benefit from greater interaction. From 2009-2013, he was the co-director of the Center for the Neural Basis of Cognition (CNBC), at Carnegie Mellon. Before joining the CMU faculty in 2009, he spent 14 years on the faculty of Brown University and 6 years on the faculty of Yale University. He received his PhD from M.I.T. in 1989 and his BA from Cornell University in 1984. The National Academy of Sciences recognized Tarr with the Troland Award in 2003, given annually to honor unusual achievement and further empirical research in psychology. The American Psychological Association recognized him with the APA Early Career Award 1997. He is a fellow of the American Psychological Association and the Society of Experimental Psychologists