Teaching Robots to See
Thursday, March 26, 2009
Presented By
Presented by the NYU School of Continuing and Professional Studies and Science & the City
In partnership with NYU's School of Continuing & Professional Studies, and the Office of the Dean of Sciences at NYU, Science & the City hosts a lecture by NYU machine learning expert Yann LeCun, who explains the science behind teaching robots to adapt to unknown environments.
Yann LeCun is Silver Professor of Computer Science at the Courant Institute of Mathematical Sciences of New York University. He received the Engineer Diploma from Ecole Supérieure d'Ingénieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987.
After a postdoctoral fellowship at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ. He became Head of the Image Processing Research Department at AT&T Labs-Research in 1996. After a short tenure at the NEC Research Institute in Princeton, he joined the NYU faculty in 2003.
Yann's research interests include computational and biological models of learning and perception, computer vision, mobile robotics, computational neuroscience, data compression, and digital libraries. His image compression technology, called DjVu, is used by numerous digital libraries and publishers to distribute scanned documents on-line, and his handwriting recognition technology is used to process a large percentage of bank checks in the US.
His learning-based image understanding techniques are used in many industrial applications including video surveillance, document understanding, and human-computer interaction. When he is not working on his research, he enjoys playing and listening to Jazz, sailing, and tinkering with technological toys, particularly the flying kind.