Dr. Tomaso Armando Poggio stands as a pioneering figure in computational neuroscience and artificial intelligence research, holding the prestigious Eugene McDermott Professorship in MIT's Department of Brain and Cognitive Sciences. He serves as an investigator at the McGovern Institute for Brain Research, a member of the MIT Computer Science and Artificial Intelligence Laboratory, and director of both the Center for Biological and Computational Learning and the multi-institutional Center for Brains, Minds, and Machines. Born in Genoa, Italy in 1947, he earned his Doctorate in Theoretical Physics from the University of Genoa in 1971 before conducting formative research at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany from 1972 to 1981. His appointment as Associate Professor at MIT in 1981 marked the beginning of his influential academic career at the intersection of neuroscience and computer science.
Poggio pioneered regularization theory as a mathematical framework for addressing ill-posed problems in vision and learning, establishing foundational principles that continue to shape modern computational approaches. His groundbreaking research includes developing influential models of the fly's visual system and human stereovision, along with creating a quantitative theory of the ventral stream in visual cortex that underpins object recognition. With over 134,000 citations and an h-index exceeding 100, his work bridges theoretical neuroscience with practical applications in computer vision, bioinformatics, and intelligent systems. His laboratory's development of 'i-theory' has successfully simulated hierarchical architectures that replicate human vision capabilities and limitations, distinguishing them from conventional deep learning networks.
As a visionary integrator of disciplines, Poggio has mentored numerous leaders in intelligence science and engineering, including Demis Hassabis of DeepMind and Amnon Shashua of Mobileye, profoundly shaping the trajectory of artificial intelligence research globally. His laboratory maintains a distinctive research philosophy centered on the conviction that understanding learning mechanisms is fundamental to deciphering both biological and artificial intelligence. Currently focused on advancing the mathematics of deep learning while investigating the computational neuroscience of visual cortex, his work continues to forge critical connections between theoretical frameworks and neural mechanisms. Through his leadership of the Center for Brains, Minds, and Machines, he remains committed to integrating neuroscience breakthroughs, computational advances, and machine learning knowledge to achieve transformative understanding of intelligence.
