Yann LeCun

Yann André LeCun^[1] (/ləˈkʌn/ lə-KUN, French: [ləkœ̃];^[2] originally spelled Le Cun;^[2] born 8 July 1960) is a Turing Award winning French 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.^[3][4]

Yann LeCun
Yann LeCun in 2018
Born	(1960-07-08) 8 July 1960 (age 63) Soisy-sous-Montmorency, France
Alma mater	ESIEE Paris (MS) Pierre and Marie Curie University (PhD)
Known for	Deep learning
Awards	Turing Award (2018) AAAI Fellow (2019) Legion of Honour (2020)
Scientific career
Institutions	Bell Labs (1988–1996) New York University Meta
Thesis	Modèles connexionnistes de l'apprentissage (1987)
Doctoral advisor	Maurice Milgram
Website	yann.lecun.com

He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets.^[5][6] He is also one of the main creators of the DjVu image compression technology (together with Léon Bottou and Patrick Haffner). He co-developed the Lush programming language with Léon Bottou.

LeCun received the 2018 Turing Award (often referred to as the "Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning.^[7] The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning".^{[8][9][10][11][12][13]}

Early life

 

Yann LeCun at the University of Minnesota, 2014

LeCun was born at Soisy-sous-Montmorency in the suburbs of Paris. His name was originally spelled Le Cun from the old Breton form Le Cunff and was from the region of Guingamp in northern Brittany. "Yann" is the Breton form for "John".

Education

He received a Diplôme d'Ingénieur from the ESIEE Paris in 1983 and a PhD 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.^[14]

Career

Bell Labs

In 1988, he 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,^[15] the "Optimal Brain Damage" regularisation methods,^[16] and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and OCR.^[17] 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,^[18] 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.

New York University

After a brief tenure as a Fellow of the NEC Research Institute (now NEC-Labs America) in Princeton, NJ, he joined New York University (NYU) in 2003, where he is Silver Professor of Computer Science 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.^[19][20] At NYU, he has worked primarily on Energy-Based Models for supervised and unsupervised learning,^[21] feature learning for object recognition in Computer Vision,^[22] and mobile robotics.^[23]

In 2012, he became the founding director of the NYU Center for Data Science.^[24] On 9 December 2013, LeCun became the first director of Meta AI Research in New York City,^{[25][non-primary source needed][26]} and stepped down from the NYU-CDS directorship in early 2014.

In 2013, he and Yoshua Bengio co-founded the International Conference on Learning Representations, which adopted a post-publication open review process he previously advocated on his website. He was the chair and organiser of the "Learning Workshop" held every year between 1986 and 2012 in Snowbird, Utah. He is a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics^[27] at UCLA. He is the Co-Director of the Learning in Machines and Brain research program (formerly Neural Computation & Adaptive Perception) of CIFAR.^[28]

In 2016, he was the visiting professor of computer science on the "Chaire Annuelle Informatique et Sciences Numériques" at Collège de France in Paris, where he presented the "leçon inaugurale" (inaugural lecture).^[29]

Honours and awards

LeCun is a member of the US National Academy of Sciences,^[30] National Academy of Engineering and the French Académie des Sciences.

He has received honorary doctorates from IPN in Mexico City^[31] in 2016, from EPFL^[32][33] in 2018 and from Université Côte d'Azur in 2021.^[34]

In 2014, he received the IEEE Neural Network Pioneer Award and in 2015, the PAMI Distinguished Researcher Award.^{[citation needed]}

In 2018 LeCun was awarded the IRI Medal, established by the Industrial Research Institute (IRI).^[35]

In 2018, he received the Harold Pender Award given by the University of Pennsylvania.^[36]

In 2019, he received the Golden Plate Award of the American Academy of Achievement.^[37]

In 2022, he received the Princess of Asturias Award in the category "Scientific Research", along with Yoshua Bengio, Geoffrey Hinton and Demis Hassabis.^[38]

Turing Award

In March 2019, LeCun won the Turing award, sharing it with Yoshua Bengio and Geoffrey Hinton.^[39]

References

1. "Version électronique authentifiée publiée au JO n° 0001 du 01/01/2020 | Legifrance". www.legifrance.gouv.fr. Retrieved 4 January 2020.
2. "Fun Stuff". yann.lecun.com. Retrieved 20 March 2020.
3. "Artificial-intelligence pioneers win $1 million Turing Award". The Washington Post.
4. Metz, Cade (27 March 2019). "Turing Award Won by 3 Pioneers in Artificial Intelligence". The New York Times. from the original on 16 June 2021.
5. "Convolutional Nets and CIFAR-10: An Interview with Yann LeCun". No Free Hunch. 22 December 2014.
6. LeCun, Yann; Bottou, Léon; Bengio, Yoshua; Haffner, Patrick (1998). "Gradient-based learning applied to document recognition" (PDF). Proceedings of the IEEE. 86 (11): 2278–2324. doi:10.1109/5.726791. S2CID 14542261.
7. "Fathers of the Deep Learning Revolution Receive ACM A.M. Turing Award". Association for Computing Machinery. New York. 27 March 2019. Retrieved 27 March 2019.
8. Vincent, James (27 March 2019). "'Godfathers of AI' honored with Turing Award, the Nobel Prize of computing". The Verge. Retrieved 20 March 2020.
9. Ranosa, Ted (29 March 2019). "Godfathers Of AI Win This Year's Turing Award And $1 Million". Tech Times. Retrieved 20 March 2020.
10. Reporters, Telegraph (27 March 2019). "Nobel prize of tech awarded to 'godfathers of AI'". The Telegraph. Retrieved 20 March 2020.
11. Shead, Sam. "The 3 'Godfathers' Of AI Have Won The Prestigious $1M Turing Prize". Forbes. Retrieved 20 March 2020.
12. Ray, Tiernan. "Deep learning godfathers Bengio, Hinton, and LeCun say the field can fix its flaws". ZDNet. Retrieved 20 March 2020.
13. Kahn, Jeremy (27 March 2019). "Three 'Godfathers of Deep Learning' Selected for Turing Award". bloomberg.com. Retrieved 10 November 2020.
14. Y. LeCun: Une procédure d'apprentissage pour réseau a seuil asymmetrique (a Learning Scheme for Asymmetric Threshold Networks), Proceedings of Cognitiva 85, 599–604, Paris, France, 1985.
15. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541–551, Winter 1989.
16. Yann LeCun, J. S. Denker, S. Solla, R. E. Howard and L. D. Jackel: Optimal Brain Damage, in Touretzky, David (Eds), Advances in Neural Information Processing Systems 2 (NIPS*89), Morgan Kaufmann, Denver, CO, 1990.
17. Yann LeCun, Léon Bottou, Yoshua Bengio and Patrick Haffner: Gradient Based Learning Applied to Document Recognition, Proceedings of IEEE, 86(11):2278–2324, 1998.
18. Léon Bottou, Patrick Haffner, Paul G. Howard, Patrice Simard, Yoshua Bengio and Yann LeCun: High Quality Document Image Compression with DjVu, Journal of Electronic Imaging, 7(3):410–425, 1998.
19. "People – Electrical and Computer Engineering". Polytechnic Institute of New York University. Retrieved 13 March 2013.
20. "Yann LeCun's Home Page".
21. Yann LeCun, Sumit Chopra, Raia Hadsell, Ranzato Marc'Aurelio and Fu-Jie Huang: A Tutorial on Energy-Based Learning, in Bakir, G. and Hofman, T. and Schölkopf, B. and Smola, A. and Taskar, B. (Eds), Predicting Structured Data, MIT Press, 2006.
22. Kevin Jarrett, Koray Kavukcuoglu, Marc'Aurelio Ranzato and Yann LeCun: What is the Best Multi-Stage Architecture for Object Recognition?, Proc. International Conference on Computer Vision (ICCV'09), IEEE, 2009
23. Raia Hadsell, Pierre Sermanet, Marco Scoffier, Ayse Erkan, Koray Kavackuoglu, Urs Muller and Yann LeCun: Learning Long-Range Vision for Autonomous Off-Road Driving, Journal of Field Robotics, 26(2):120–144, February 2009.
24. "Center for Data Science – New York University".
25. "Yann LeCun" – via Facebook.
26. "DIRECTOR OF AI RESEARCH". 2016. from the original on 27 April 2017 – via Facebook.
27. http://www.ipam.ucla.edu/programs/gss2012/ Institute for Pure and Applied Mathematics
28. "Neural Computation & Adaptive Perception Advisory Committee Yann LeCun". CIFAR. Retrieved 16 December 2013.
29. "L'apprentissage profond : une révolution en intelligence artificielle". college-de-france.fr. 28 August 2015. Retrieved 1 March 2022.
30. "News from the National Academy of Sciences". 26 April 2021. Retrieved 4 July 2021. Newly elected members and their affiliations at the time of election are: … LeCun, Yann; vice president and chief artificial intelligence scientist, Facebook; and Silver Professor of Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering, New York University, New York City, entry in member directory:"Member Directory". National Academy of Sciences. Retrieved 4 July 2021.
31. "Primera generación de Doctorados Honoris Causa en el IPN". Retrieved 11 October 2016.
32. Aubort, Sarah (10 August 2018). "EPFL celebrates 1,043 new Master's graduates". Retrieved 27 January 2019.
33. "Yann LeCun @EPFL – "Self-supervised learning: could machines learn like humans?"". Archived from the original on 21 December 2021. Retrieved 27 January 2019 – via YouTube.
34. "YANN LECUN, DOCTEUR HONORIS CAUSA D'UNIVERSITÉ CÔTE D'AZUR".
35. IRI Medal 2018
36. "2018 Harold Pender Award and Lecture: Yann LeCun". Retrieved 22 May 2019.
37. "Golden Plate Awardees of the American Academy of Achievement". achievement.org. American Academy of Achievement.
38. Princess of Asturias Awards 2022
39. Metz, Cade (27 March 2019). "Three Pioneers in Artificial Intelligence Win Turing Award". The New York Times. ISSN 0362-4331. Retrieved 27 March 2019.

External links

• Yann LeCun's personal website
• Yann LeCun's lab website at NYU
• Yann LeCun's website at Collège de France
• Yann LeCun's List of PhD Students
• Yann LeCun's publications
• Convolutional Neural Networks
• DjVuLibre website
• Lush website
• AMA: Yann LeCun (self.MachineLearning) www.reddit.com Ask Me Anything : Yann LeCun
• IEEE Spectrum Article
• Technology Review article