Tomaso Poggio

Tomaso Poggio
Tomaso
Poggio
Director
Research Thrust Leader

Department: 

BCS

Associated Research Thrust: 

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). A former Corporate Fellow of Thinking Machines Corporation and a former director of PHZ Capital Partners, Inc., is a director of Mobileye and was involved in starting, or investing in, several other high tech companies including Arris Pharmaceutical, nFX, Imagen, Digital Persona and Deep Mind. Among his PhD students and post-docs are some of the today’s leaders in the Science and in the Engineering of Intelligence,  from Christof Koch (President and Chief Scientific Officer, Allen Institute) to Amnon Shashua (CTO and founder, Mobileye) and Demis Hassabis (CEO and founder, Deep Mind).

Email: 

Advisees

Guy Ben-Yosef - Postdoctoral Associate
Xavier Boix - Postdoctoral Fellow
Ira Ceka - Visiting Research Intern
Georgios Evangelopoulos - Research Scientist
Noah Golowich - Visiting Student
Yena Han - Graduate Student
Gabe Hege - Graduate Student
Erwin Hilton - Graduate Student
Wouter Kool - Postdoctoral Fellow
Owen Lewis - Graduate Student
Qianli Liao - Graduate Student
Boying Meng - Graduate Student
Brando Miranda - Research Assistant
Carlos Ponce - Postdoc
Tomotake Sasaki - Visiting Scientist
Chiyuan Zhang - Graduate Student

Projects

Publications

H. Mhaskar, Liao, Q., and Poggio, T., When and Why Are Deep Networks Better Than Shallow Ones?, AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence. 2017.
A. Tacchetti, Voinea, S., Evangelopoulos, G., and Poggio, T., Representation Learning from Orbit Sets for One-shot Classification, in AAAI Spring Symposium Series, Science of Intelligence, AAAI, 2017.
Y. Han, Roig, G., Geiger, G., and Poggio, T., Is the Human Visual System Invariant to Translation and Scale?, in AAAI Spring Symposium Series, Science of Intelligence, 2017.
J. Mutch, Anselmi, F., Tacchetti, A., Rosasco, L., Leibo, J. Z., and Poggio, T., Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex, in Computational and Cognitive Neuroscience of Vision, Springer, 2017, pp. 85-104.
O. Lewis and Poggio, T., Object and Scene Perception, in From Neuron to Cognition via Computational Neuroscience, Cambridge, MA, USA: The MIT Press, 2016.
Q. Liao, Leibo, J. Z., and Poggio, T., How Important Is Weight Symmetry in Backpropagation?, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ., 2016.
M. Nickel, Rosasco, L., and Poggio, T., Holographic Embeddings of Knowledge Graphs, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, USA, 2016.
F. Bach and Poggio, T., Introduction Special issue: Deep learning, Information and Inference, vol. 5, pp. 103-104, 2016.
F. Anselmi, Rosasco, L., and Poggio, T., On invariance and selectivity in representation learning, Information and Inference: A Journal of the IMA, p. iaw009, 2016.
T. Poggio and Anselmi, F., Visual Cortex and Deep Networks: Learning Invariant Representations. Cambridge, MA, USA: The MIT Press, 2016, p. 136.
T. Poggio, Deep Leaning: Mathematics and Neuroscience, A Sponsored Supplement to Science, vol. Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, pp. 9-12, 2016.
Q. Liao, Leibo, J. Z., and Poggio, T., How Important Is Weight Symmetry in Backpropagation?, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ., 2016.
C. Frogner, Zhang, C., Mobahi, H., Araya-Polo, M., and Poggio, T., Learning with a Wasserstein Loss, in Advances in Neural Information Processing Systems (NIPS 2015) 28, 2015.
A. Tacchetti, Isik, L., and Poggio, T., Invariant representations for action recognition in the visual system., Vision Sciences Society, vol. 15, no. 12. Journal of vision, 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.
C. Zhang, Voinea, S., Evangelopoulos, G., Rosasco, L., and Poggio, T., Phone Classification by a Hierarchy of Invariant Representation Layers, in INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association, Singapore, 2014.
J. Z. Leibo, Liao, Q., and Poggio, T., Subtasks of Unconstrained Face Recognition. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. (VISAPP)., Lisbon, Portugal, 2014.
C. Zhang, Evangelopoulos, G., Voinea, S., Rosasco, L., and Poggio, T., A Deep Representation for Invariance and Music Classification, in ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014.
S. Villa, Rosasco, L., Poggio, T., Schölkopf, B., Luo, Z., and Vovk, V., On Learnability, Complexity and Stability, in Empirical Inference, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 59 - 69.
E. Lattman, Poggio, T., and Westervelt, R., NSF Science and Technology Centers – The Class of 2013. North America Gender Summit, Washington, D.C., 2013.