Yann LeCun
Computer scientist
New York University and Meta AI
United States
Yann LeCun is a researcher affiliated with New York University and Meta AI. This profile connects verified scholarly identity information and publication records from OpenAlex and DOI sources. Research areas include Neural Networks and Applications, Domain Adaptation and Few-Shot Learning, Advanced Image and Video Retrieval Techniques, Advanced Neural Network Applications, Generative Adversarial Networks and Image Synthesis.
Read biography ↓Biography, research & contributions
Research profile
Yann LeCun is a computer scientist known for neural-network methods for recognising visual patterns. He shared the 2018 ACM A. M. Turing Award with Geoffrey Hinton and Yoshua Bengio. His work helped demonstrate that learning systems could solve practical recognition problems.
Source: ACM: the 2018 Turing Award ↗
Convolutional networks in context
Document recognition involves more than assigning a label to an isolated image. A system may need to locate fields, separate characters, recognise them and interpret the resulting sequence. LeCun and colleagues studied how gradient-based learning could be used within such systems. Their work connects the design of a neural-network architecture with the requirements of a real recognition task.
Source: LeCun and colleagues: gradient-based document recognition ↗
Selected publication and collaborators
Gradient-Based Learning Applied to Document Recognition was published in Proceedings of the IEEE in 1998, with Léon Bottou, Yoshua Bengio and Patrick Haffner. The author-hosted record provides the full paper and bibliographic information. It is a substantial technical resource for readers studying the relationship between neural learning and handwritten-character recognition.
How to explore the work
Begin with the ACM award background for the broader research context, then use the 1998 paper to examine architectures, experiments and system design. When comparing recognition methods, pay attention to the task, data and evaluation procedure. A result on one benchmark is evidence about that setting, rather than proof that one architecture is best for every application.
At a glance
- Full name
- Yann LeCun
- Fields
- Artificial Intelligence, Computer Science
- OpenAlex ID
- A5001226970
Research interests
- Computer vision
- Convolutional neural networks
- Representation learning
- Document recognition
Research topics
Education
Not yet documented in this profile.
Selected research & further reading
A curated reading list, not a ranking by citation count. References use DOI metadata, matching public scholarly records or authoritative lecture sources.
- Deep learning ↗2015 · NatureDOI: 10.1038/nature14539
- Gradient-based learning applied to document recognition ↗1998 · Proceedings of the IEEEDOI: 10.1109/5.726791
Citation & publication trends
Annual counts are not yet available from a verified author record. Explore the research guide above for the work itself; publication and citation totals depend on database coverage.
Most-cited linked publications
- Multi-modal AI for comprehensive breast cancer prognostication2026-05-20 · Nature Communications6record citations
- Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning2026-08-20 · Nature Communications4record citations
- Chimère Ω — blueprint for a physico-cognitively inspired local-first LLM runtime2025-11-11 · arXiv (Cornell University)2record citations
- VL-JEPA: Joint Embedding Predictive Architecture for Vision-language2025-12-11 · arXiv (Cornell University)1record citations
- What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?2025-12-30 · HAL (Le Centre pour la Communication Scientifique Directe)0record citations
Recent linked publications
- Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning2026-08-20 · Nature Communications
- Multi-modal AI for comprehensive breast cancer prognostication2026-05-20 · Nature Communications
- What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?2025-12-30 · HAL (Le Centre pour la Communication Scientifique Directe)
- World Models for Learning Dexterous Hand-Object Interactions from Human Videos2025-12-15 · arXiv (Cornell University)
- VL-JEPA: Joint Embedding Predictive Architecture for Vision-language2025-12-11 · arXiv (Cornell University)
Journals published in
Related publishers
Institutions
Current verified institution
No verified record links added yet.
Previous institutions
No verified record links added yet.
Awards & honors
- 2018 — ACM A. M. Turing Award, shared with Geoffrey Hinton and Yoshua Bengio
Career timeline
- 2018 — ACM A. M. Turing Award, shared with Geoffrey Hinton and Yoshua Bengio
Co-authors
Education & career institution links
No verified record links added yet.
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Sign in to claim this profileSources & data information
Editorial review: 2026-10-03 · Last data update: 2026-10-03
- https://openalex.org/A5001226970
- https://awards.acm.org/binaries/content/assets/press-releases/2019/march/turing-award-2018.pdf
- https://leon.bottou.org/papers/lecun-98h
Unknown values are left blank. Linked publications may be a subset of total works. Identity verification, data retrieval and profile ownership are separate checks.
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