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Yann LeCun
Yann André Le Cun (/ləˈkʌn/ lə-KUN, French: [ləkœ̃]; usually spelled LeCun; born 8 July 1960) is a French-American computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice President, Chief AI Scientist at Meta.
He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image compression technology, alongside Léon Bottou and Patrick Haffner. He co-developed the Lush programming language with Léon Bottou.
In 2018, LeCun, Yoshua Bengio, and Geoffrey Hinton, received the Turing Award for their work on deep learning. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning".
LeCun was born on 8 July 1960, at Soisy-sous-Montmorency in the suburbs of Paris. His name, Le Cun, originates from the old Breton form Le Cunff, and was from the region of Guingamp in northern Brittany. "Yann" is the Breton form for "John".
He received a Diplôme d'Ingénieur from the ESIEE Paris in 1983 and a Ph.D. in computer science from Université Pierre et Marie Curie (today Sorbonne University) in 1987 during which he proposed an early form of the back-propagation learning algorithm for neural networks. Before joining AT&T, LeCun was a postdoc for a year, starting in 1987, under Geoffrey Hinton at the University of Toronto.
In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called convolutional neural networks (LeNet), the "Optimal Brain Damage" regularization methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and Optical character recognition (OCR). The bank check recognition system that he helped develop was widely deployed by NCR and other companies, reading over 10% of all the checks in the US in the late 1990s and early 2000s.[citation needed]
In 1996, he joined AT&T Labs-Research as head of the Image Processing Research Department, which was part of Lawrence Rabiner's Speech and Image Processing Research Lab, and worked primarily on the DjVu image compression technology, used by many websites, notably the Internet Archive, to distribute scanned documents.[citation needed] His collaborators at AT&T include Léon Bottou and Vladimir Vapnik.
After a brief tenure as a Fellow of the NEC Research Institute (now NEC-Labs America) in Princeton, NJ, LeCun joined New York University (NYU) in 2003, where he is Jacob T. Schwartz Chaired Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. He is also a professor at the Tandon School of Engineering. At NYU, he has worked primarily on Energy-Based Models for supervised and unsupervised learning, feature learning for object recognition in Computer Vision, and mobile robotics.
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Yann LeCun
Yann André Le Cun (/ləˈkʌn/ lə-KUN, French: [ləkœ̃]; usually spelled LeCun; born 8 July 1960) is a French-American computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice President, Chief AI Scientist at Meta.
He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs). He is also one of the main creators of the DjVu image compression technology, alongside Léon Bottou and Patrick Haffner. He co-developed the Lush programming language with Léon Bottou.
In 2018, LeCun, Yoshua Bengio, and Geoffrey Hinton, received the Turing Award for their work on deep learning. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning".
LeCun was born on 8 July 1960, at Soisy-sous-Montmorency in the suburbs of Paris. His name, Le Cun, originates from the old Breton form Le Cunff, and was from the region of Guingamp in northern Brittany. "Yann" is the Breton form for "John".
He received a Diplôme d'Ingénieur from the ESIEE Paris in 1983 and a Ph.D. in computer science from Université Pierre et Marie Curie (today Sorbonne University) in 1987 during which he proposed an early form of the back-propagation learning algorithm for neural networks. Before joining AT&T, LeCun was a postdoc for a year, starting in 1987, under Geoffrey Hinton at the University of Toronto.
In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called convolutional neural networks (LeNet), the "Optimal Brain Damage" regularization methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and Optical character recognition (OCR). The bank check recognition system that he helped develop was widely deployed by NCR and other companies, reading over 10% of all the checks in the US in the late 1990s and early 2000s.[citation needed]
In 1996, he joined AT&T Labs-Research as head of the Image Processing Research Department, which was part of Lawrence Rabiner's Speech and Image Processing Research Lab, and worked primarily on the DjVu image compression technology, used by many websites, notably the Internet Archive, to distribute scanned documents.[citation needed] His collaborators at AT&T include Léon Bottou and Vladimir Vapnik.
After a brief tenure as a Fellow of the NEC Research Institute (now NEC-Labs America) in Princeton, NJ, LeCun joined New York University (NYU) in 2003, where he is Jacob T. Schwartz Chaired Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. He is also a professor at the Tandon School of Engineering. At NYU, he has worked primarily on Energy-Based Models for supervised and unsupervised learning, feature learning for object recognition in Computer Vision, and mobile robotics.