Yann André Le Cun (born 8 July 1960) is a French-American computer scientist specializing in artificial intelligence, machine learning, computer vision, robotics, and image compression. He is the Jacob T. Schwartz Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University. He served as Chief AI Scientist at Meta Platforms before co-founding Advanced Machine Intelligence Labs in December 2025. LeCun is widely recognized for his work on optical character recognition and computer vision using convolutional neural networks, and he co-developed the DjVu image compression technology and the Lush programming language with Léon Bottou.
In 2018, LeCun, Yoshua Bengio, and Geoffrey Hinton received the Turing Award from the Association for Computing Machinery for their contributions to deep learning. Together, they are often called the "Godfathers of AI" or "Godfathers of Deep Learning." LeCun's research has shaped modern Machine learning and Artificial intelligence, particularly through his advocacy for energy-based models and world models.
Early Life and Education
LeCun was born in Soisy-sous-Montmorency, a suburb of Paris, France. His surname, Le Cun, derives from the old Breton form Le Cunff, originating from Guingamp in northern Brittany. He received 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, LeCun was a postdoctoral researcher at the University of Toronto, supervised by Geoffrey Hinton. He later became a naturalized American citizen. LeCun has three sons, and his brother works at Google.
Career at Bell Labs
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 convolutional neural networks (CNNs), including the LeNet architecture, which became foundational for image recognition. He also introduced the "Optimal Brain Damage" regularization method and Graph Transformer Networks, a precursor to conditional random fields. His work enabled practical handwriting recognition and optical character recognition (OCR), leading to bank check recognition systems 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. He focused on DjVu, a compression format for scanned documents, later used by the Internet Archive. His collaborators included LĂ©on Bottou and Vladimir Vapnik.
Academic Career at NYU
After a brief fellowship at NEC Research Institute, LeCun joined New York University in 2003. He holds the Jacob T. Schwartz Chair in Computer Science and Neural Science at the Courant Institute 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, LeCun became the founding director of the NYU Center for Data Science. In 2013, he co-founded the International Conference on Learning Representations (ICLR) with Yoshua Bengio, which adopted a post-publication open review process. He also organized the annual "Learning Workshop" in Snowbird, Utah, from 1986 to 2012. LeCun served as co-director of CIFAR's Learning in Machines and Brain program and was a visiting professor at Collège de France in 2016. He is a scientific advisor to the French research group Kyutai.
Leadership at Meta Platforms
LeCun joined Facebook (now Meta Platforms) in 2013 as chief AI scientist, becoming the first director of Meta AI Research in New York City. He led FAIR, the company's AI research laboratory, until 2025. During his tenure, he advocated for self-supervised learning and criticized the limitations of large language models for achieving human-level intelligence.
In November 2025, LeCun announced his departure from Meta to focus on his own venture. He co-founded Advanced Machine Intelligence Labs (AMI Labs) in December 2025, with CEO Alex LeBrun and LeCun as Executive Chair. AMI Labs aims to build AI "world models" that understand physical world dynamics rather than merely predicting text. In March 2026, AMI raised $1.03 billion 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, a company developing energy-based reasoning systems.
Contributions to Deep Learning
LeCun's most significant contribution is the development of convolutional neural networks, which mimic biological visual processing and are now standard in computer vision. His early work on backpropagation and CNNs laid the groundwork for modern Deep learning. He also pioneered energy-based models, which learn to assign low energy to correct predictions and high energy to incorrect ones, influencing later architectures.
His research on graph transformer networks and regularization methods has been widely adopted. LeCun has been a vocal proponent of open research and has published extensively on topics ranging from Data Augmentation to Loss Functions. His ideas have influenced transformers and generative AI systems, though he remains critical of purely text-based 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 2019, he shared the 2018 Turing Award with Yoshua Bengio and Geoffrey Hinton. In 2022, he received the Princess of Asturias Award in the category "Scientific Research."
Legacy and Views
LeCun is considered one of the founding figures of modern AI. His advocacy for world models and energy-based approaches contrasts with the dominant paradigm of large language models. He has argued that true intelligence requires understanding the physical world, not just statistical patterns in text. His work continues to influence research at institutions like MIT CSAIL and Stanford AI Lab, and his leadership at AMI Labs signals a new direction for AI development.
Despite his contributions, LeCun has also been a controversial figure, often debating the risks of AI and the path to superintelligence. He remains an active researcher and public speaker, shaping the future of artificial intelligence.