Yann André Le Cun (born 8 July 1960) is a French-American computer scientist recognized for foundational contributions to artificial intelligence, machine learning, computer vision, and image compression. He is best known for developing convolutional neural networks (CNNs), which became a cornerstone of modern Deep learning systems, and for co-creating the DjVu image compression technology. LeCun is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University and served as Chief AI Scientist at Meta Platforms before co-founding Advanced Machine Intelligence Labs in December 2025.
LeCun's research has shaped both academic and industrial AI. His work on handwriting recognition and bank check processing in the 1990s demonstrated the practical viability of Neural networks, while his later advocacy for energy-based models and world models continues to influence debates about the future of Artificial intelligence. In 2018, he shared the Turing Award with Yoshua Bengio and Geoffrey Hinton for their collective contributions to deep learning, cementing his status as one of the field's most influential figures.
Early life and education
LeCun was born on 8 July 1960 in Soisy-sous-Montmorency, a suburb of Paris. His surname, Le Cun, derives from the old Breton form Le Cunff and originates from the Guingamp region of northern Brittany; Yann is the Breton form of Jean. He earned a Diplôme d'Ingénieur from ESIEE Paris in 1983 and a PhD in computer science from Université Pierre et Marie Curie (now Sorbonne University) in 1987. During his doctoral studies, he proposed an early form of backpropagation, an algorithm essential for training neural networks. From 1987 to 1988, he was a postdoctoral researcher at the University of Toronto, supervised by Geoffrey Hinton. LeCun has three sons and holds American citizenship.
Career at Bell Labs and AT&T
In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, led by Lawrence D. Jackel. There, he developed key machine learning methods, including a biologically inspired model of image recognition called convolutional neural networks (LeNet), the "Optimal Brain Damage" regularization technique, and Graph Transformer Networks (similar to conditional random fields). These methods were applied to handwriting recognition and optical character recognition (OCR). The bank check recognition system he helped develop was widely deployed by NCR and other companies.
In 1996, LeCun became head of the Image Processing Research Department at AT&T Labs-Research, part of Lawrence Rabiner's Speech and Image Processing Research Lab. His work there focused on the DjVu image compression technology, designed for efficient distribution of scanned documents and later used by the Internet Archive. His collaborators included Léon Bottou and Vladimir Vapnik. He also co-developed the Lush programming language with Bottou.
New York University and academic leadership
After a brief fellowship at NEC Research Institute, LeCun joined New York University in 2003. He became the Jacob T. Schwartz Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. His research at NYU centered on energy-based models for supervised and unsupervised learning, feature learning for object recognition, and mobile robotics. In 2012, he founded the NYU Center for Data Science and served as its director until early 2014.
LeCun co-founded the International Conference on Learning Representations (ICLR) in 2013 with Yoshua Bengio, advocating for a post-publication open review process. He also organized the annual "Learning Workshop" in Snowbird, Utah, from 1986 to 2012. He has been a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics at UCLA and co-director of the Learning in Machines and Brain program at CIFAR. In 2016, he was a visiting professor at the Collège de France in Paris, delivering the inaugural lecture. He also serves as a scientific advisor to the French research group Kyutai.
Meta Platforms and FAIR
LeCun joined Facebook (now Meta Platforms) in 2013 as chief AI scientist, leading the company's AI research laboratory, FAIR. During his tenure, he oversaw research that contributed to advances in self-supervised learning, Large language models, and multimodal AI. His role involved guiding Meta's long-term AI strategy, including open-source initiatives and foundational research in areas like Transformer (architecture) architectures and Generative AI. He remained at Meta until 2025, when he announced his departure to pursue a new venture.
Advanced Machine Intelligence Labs and later ventures
On 19 November 2025, LeCun confirmed he would leave Meta after ten years to found Advanced Machine Intelligence Labs (AMI Labs), a company focused on world-model architectures and human-like artificial intelligence he terms "superintelligence." The company is run by CEO Alex LeBrun, with LeCun serving as Executive Chair. AMI Labs aims to build AI systems that learn to understand the physical world's structure and dynamics, rather than merely predicting text like traditional Large language models. In March 2026, AMI announced it had raised $1.03 billion in funding at a $3.5 billion pre-money valuation, with investors including Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions.
In January 2026, LeCun became founding chair of the Technical Research Board of Logical Intelligence, an AI company developing energy-based reasoning systems. This role aligns with his long-standing interest in energy-based models as an alternative to probabilistic approaches.
Honours and awards
LeCun is a member of the US National Academy of Sciences, the National Academy of Engineering, and the French Académie des Sciences. He has received honorary doctorates from Instituto Politécnico Nacional (2016), EPFL (2018), Université Côte d'Azur (2021), Università di Siena (2023), and Hong Kong University of Science and Technology (2023). His awards include the IEEE Neural Network Pioneer Award (2014), the PAMI Distinguished Researcher Award (2015), the IRI Medal (2018), the Harold Pender Award (2018), and the Golden Plate Award of the American Academy of Achievement (2019). In March 2019, he won the 2018 Turing Award, sharing it with Yoshua Bengio and Geoffrey Hinton. In 2022, he received the Princess of Asturias Award in the category "Scientific Research."
Legacy and influence
LeCun's development of convolutional neural networks laid the groundwork for modern computer vision, enabling breakthroughs in image recognition, autonomous driving, and medical imaging. His early work on backpropagation helped revive interest in neural networks during a period of skepticism. As a public intellectual, he has been vocal about the limitations of current AI approaches, arguing that large language models are insufficient for achieving human-level intelligence and advocating for world models that incorporate physical understanding. His transition from academia to industry and back exemplifies the close ties between AI research and commercial applications, and his ongoing ventures continue to shape the direction of the field.