Tomaso A. Poggio

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology, an investigator at the McGovern Institute for Brain Research, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM).

Research Interests

Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience, machine learning, and computer vision. His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include:

• Mathematical foundations of statistical learning theory
• Engineering applications in computer vision, graphics, bioinformatics, and intelligent search
• Computational neuroscience of visual object recognition and the ventral stream of the visual cortex

Scientific Awards & Honors

• Eugene McDermott Professorship, MIT

Advising & Grants

Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013.

Laboratories & Teams

Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.