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Tomaso A. Poggio


CBCL- Massachusetts Institute of Technology

Tomaso A. Poggio

Tomaso A. Poggio is one of the founders of computational neuroscience. He pioneered models of the fly's visual system and of human stereovision, introduced regularization theory to computational vision, made key contributions to the biophysics of computation and to learning theory, developed an influential model of recognition in the visual cortex. He is one of the most cited computational neuroscientists (with a h-index greater than 90 – based on GoogleScholar).

He is the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences; Co-Director, Center for Biological and Computational Learning; Member for the last 27 years of the Computer Science and Artificial Intelligence Laboratory at MIT; since 2000, member of the faculty of the McGovern Institute for Brain Research. He received his Doctor in Theoretical Physics from the University of Genoa in 1971 and was a Wissenschaftlicher Assistant, Max Planck Institut für Biologische Kybernetik, Tüebingen, Germany from 1972 until 1981 when he became Associate Professor at MIT. He 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 several awards such as the Otto-Hahn-Medaille Award of the Max-Planck-Society, the Max Planck Research Award (with M. Fahle), from the Alexander von Humboldt Foundation, the MIT 50K Entrepreneurship Competition Award, the Laurea Honoris Causa from the University of Pavia in 2000 (Volta Bicentennial), the 2003 Gabor Award, the 2009 Okawa prize, and named an American Association for the Advancement of Science (AAAS) Fellow (2009).

Hierarchical Learning Machines and Neuroscience of Visual Cortex

Learning is the gateway to understanding intelligence and to reproducing it in machines. A classical example of learning algorithms is provided by regularization in Reproducing Kernel Hilbert Spaces. The corresponding architecture however is different from the deep hierarchies found in the brain. I will sketch a new attempt (with S. Smale) to develop a mathematics for hierarchical kernel machines – centered around the notion of a recursively defined “derived kernel” – and directly suggested by the neuroscience of the visual cortex.

Relevant papers can be downloaded from http://cbcl.mit.edu/publications/index-pubs.html

Videolecture





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