About
Tomaso A. Poggio is the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences; Director, Center for Brains, Minds and Machines; Member of the Computer Science and Artificial Intelligence Laboratory at MIT; since 2000, member of the faculty of the McGovern Institute for Brain Research.
Born in Genoa, Italy (naturalized in 1994), 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, the American Association for the Advancement of Science (AAAS) Fellowship (2009) and the Swartz Prize for Theoretical and Computational Neuroscience in 2014. He is one of the most cited computational neuroscientists (with a h-index greater than 100 – based on GoogleScholar).
Research
Computational Neuroscience
Our scientific goal is to discover how intelligence is grounded in computation, how these computations are implemented in neural systems, how they develop during childhood, and how social interaction amplifies the power of these computations. As we progress, we will aggressively pursue opportunities to discover and develop unifying mathematical theories. To foster collaboration across disciplines, we will jointly develop top-to-bottom computational models powerful enough to explain visually perceived situations the way humans do. The models will emerge from fundamental questions about visually perceived situations: who, what, why, where, how, with what motives, with what purpose, and with what expectations. Models of visual understanding will be further advanced by developing computational models of what children know and learn about physical objects and intentional agents, and how they learn so much so rapidly. We will develop computational models of learning, memory, reasoning, and concept formation that are consistent with behavior, neural systems, and neural circuits. We will also develop computational models that enable computers to think new thoughts, imagine new scenes, form hypotheses, propose interventions, and compose narratives. Through these collaborative efforts, we will develop new methodologies and new technologies that will help to reach our goals. Our diversity goal is to ensure that the field of Science and Engineering of Intelligence is broadly inclusive. Our education goal is to ensure that our new knowledge is packaged in accessible ways, including model subjects at graduate and undergraduate levels. Our knowledge transfer goal is to ensure that new knowledge is quickly and broadly disseminated and brought to bear on the great challenges of the 21st century, so as to serve the people of the nation and the world.
Teaching
9.520 Statistical Learning Theory and Applications
9.S915 Aspects of a Computational Theory of Intelligence
Publications
C. Zhang, Voinea, S., Evangelopoulos, G., Rosasco, L., and Poggio, T., “Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations”, in INTERSPEECH 2015, Submitted.
F. Anselmi, Rosasco, L., and Poggio, T., “On Invariance and Selectivity in Representation Learning”. 2015.
F. Anselmi, Rosasco, L., Tan, C., and Poggio, T., “Deep Convolutional Networks are Hierarchical Kernel Machines”. 2015.
S. Voinea, Zhang, C., Evangelopoulos, G., Rosasco, L., and Poggio, T., “Word-level Invariant Representations From Acoustic Waveforms”, in INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association, Singapore, 2014.