Yann LeCun

Yann André LeCun is a Turing Award winning French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience.

Yann André LeCun 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.

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. 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. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning".


LeCun was born at Soisy-sous-Montmorency in the suburbs of Paris. 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.

LeCun's journey in the world of machine learning began in 1988 when he joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, under the leadership of Lawrence D. Jackel. During his time at Bell Labs, he developed several innovative machine learning methods, including the widely recognized convolutional neural networks (CNNs). CNNs, inspired by the biological processes of visual perception, have had a profound impact on image recognition and computer vision.

In addition to CNNs, LeCun introduced the "Optimal Brain Damage" regularization methods and the Graph Transformer Networks method, which found applications in handwriting recognition and optical character recognition (OCR). Notably, his contributions played a crucial role in the development of a bank check recognition system that became widely adopted by companies like NCR, processing a significant portion of checks in the United States during the late 1990s and early 2000s.

In 1996, Yann LeCun transitioned to AT&T Labs-Research, where he led the Image Processing Research Department. During this period, he focused on the development of DjVu, an image compression technology utilized by numerous websites, including the Internet Archive, for distributing scanned documents. Collaborating with experts such as Léon Bottou and Vladimir Vapnik, he continued to push the boundaries of image processing and machine learning.

In 2003, LeCun embarked on a new chapter in his career when he joined New York University (NYU) as a Silver Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. He also held a professorship at the Tandon School of Engineering at NYU. At NYU, his research expanded into areas such as Energy-Based Models for supervised and unsupervised learning, feature learning for object recognition in computer vision, and mobile robotics.

In recognition of his contributions to the field, LeCun was appointed as the founding director of the NYU Center for Data Science in 2012. Later, in 2013, he assumed the role of the first director of Meta AI Research in New York City. Concurrently, he co-founded the International Conference on Learning Representations (ICLR) with Yoshua Bengio, advocating for a post-publication open review process.

LeCun's commitment to advancing machine learning and artificial intelligence extended beyond academia. He chaired the annual "Learning Workshop" for many years and became a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics at UCLA. He also co-directed the Learning in Machines and Brain research program (formerly Neural Computation & Adaptive Perception) of the Canadian Institute for Advanced Research (CIFAR).

In 2016, Yann LeCun held the position of visiting professor of computer science as part of the "Chaire Annuelle Informatique et Sciences Numériques" at Collège de France in Paris, where he delivered an inaugural lecture. Throughout his career, LeCun's pioneering work has had a profound and lasting impact on the fields of machine learning, artificial intelligence, and computer vision, making him a celebrated figure in the world of science and technology.


Yann LeCun's vision revolves around the relentless pursuit of artificial intelligence and machine learning that not only matches but also surpasses human capabilities. He envisions a future where machines possess the ability to understand, reason, and learn from vast datasets, ultimately making them indispensable partners in solving complex real-world problems. LeCun's vision emphasizes the development of intelligent systems that can perceive the world, interpret it, and adapt to new challenges with a level of proficiency and efficiency that redefines human-machine interactions.

Furthermore, LeCun envisions democratizing AI, making these transformative technologies accessible to a broader spectrum of society. He believes in fostering collaboration across academia, industry, and the public sector to harness the full potential of AI in addressing global challenges. In this vision, AI systems are not just tools but intelligent companions that augment human capabilities, enhancing our understanding of the world and contributing to a more sustainable, equitable, and interconnected future.

Recognition and Awards
He is a distinguished member of the US National Academy of Sciences, the National Academy of Engineering, and the French Académie des Sciences, showcasing his global impact on the AI community. LeCun's exceptional work has been acknowledged with several honorary doctorates, including those from IPN in Mexico City in 2016, EPFL in 2018, and Université Côte d'Azur in 2021, further cementing his influence on the international scientific community. In recognition of his pioneering contributions to neural networks, he received the IEEE Neural Network Pioneer Award in 2014 and the PAMI Distinguished Researcher Award in 2015. LeCun's dedication to advancing industrial research was honored with the IRI Medal in 2018 and the Harold Pender Award from the University of Pennsylvania in the same year. His enduring impact on the field was celebrated with the Golden Plate Award from the American Academy of Achievement in 2019. Additionally, in 2022, LeCun was awarded the prestigious Princess of Asturias Award in Scientific Research, alongside fellow luminaries Yoshua Bengio, Geoffrey Hinton, and Demis Hassabis, recognizing their transformative contributions to the world of AI. The pinnacle of his achievements came in 2019 when he was jointly awarded the Turing Award, the highest honor in computer science. This distinguished recognition was shared with Yoshua Bengio and Geoffrey Hinton, underscoring their groundbreaking work in deep learning and neural networks, which has reshaped the landscape of artificial intelligence.



Yann LeCun
computer scientist, specialist in machine learning, computer vision, mobile robotics, computational neuroscience
Known for
Deep learning (Godfather), AI (Godfather)
Turing Award (2018), AAAI Fellow (2019), Legion of Honour (2020)
ESIEE Paris (MS), Pierre and Marie Curie University (PhD)
Social Media
Mon Feb 26 2024

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