Tomaso Poggio

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Tomaso Armando Poggio (born 1947, Genoa, Italy), is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences, an investigator at the McGovern Institute for Brain Research, a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the director of the Center for Biological and Computational Learning at MIT.

Biography[edit]

Born in Genoa, Italy, and educated at Istituto Arecco, Tomaso Poggio completed his doctorate in physics at the University of Genoa and received his degree in Theoretical Physics under professor A. Borsellino.

His interdisciplinary research on the problem of intelligence, between brains and computers, started at the Max Planck Institute in Tuebingen, Germany in collaborations with Werner E. Reichardt, David C. Marr and Francis H.C. Crick, among others. He has made contributions to learning theory, to the computational theory of vision, to the understanding of the fly's visual system, and to the biophysics of computation. His recent work is focused on computational neuroscience in close collaboration with several physiology labs, trying to answer the questions of how our visual system learns to see and recognize scenes and objects.[1]

Poggio is an honorary member of the Neuroscience Research Program, a member of the American Academy of Arts and Sciences and a founding fellow of AAAI. He received the Laurea Honoris Causa in Computer Engineering from the University of Pavia in 2000, the 2003 Gabor Award, the 2009 Okawa prize [1], and named in 2009 a Fellow of the American Association for the Advancement of Science (AAAS) for “distinguished contributions to computational neuroscience, in particular, computational vision learning and regularization theory, biophysics of computation and models of recognition in the visual cortex.” He is one of the most cited computational neuroscientists.[citation needed] A former corporate fellow of Thinking Machines Corporation, he was involved in starting several other high tech companies.

Honors[edit]

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References[edit]

  1. ^ A feedforward architecture accounts for rapid categorization. Serre T, Oliva A, Poggio T. Proceedings of the National Academy of Sciences of the United States of America. 2007 Apr 10;104(15):6424-9.

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