Yann LeCun: h-index, Total Citations, and Citation Map
Yann LeCun's h-index is 172 (469 i10-index, 469,858+ total citations across 751+ publications) according to Google Scholar as of May 2026. Yann LeCun is affiliated with Chief AI Scientist at Facebook & JT Schwarz Professor at the Courant Institute, New York University.
Yann LeCun is a researcher affiliated with Chief AI Scientist at Facebook & JT Schwarz Professor at the Courant Institute, New York University, specializing in AI, machine learning, computer vision. Their work has been cited 469,858 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Yann LeCun's Citation Metrics
Bibliometric impact based on 751 indexed publications.
- H-Index
- 172
- i10-Index
- 469
- Total Citations
- 469,858
- Citing Countries
- 80
As of May 2026.
Yann LeCun has an h-index of 172 and 469,858 total citations across 751 publications, with research cited by institutions in 80 countries.
Global Impact Map
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Top Cited Works
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Deep learning
2015115,491
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
2821 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered gradient-based learning for pattern recognition, establishing foundational methods for handwritten digit and document classification that evolved into character-level text analysis.
The researcher pioneered foundational convolutional network architectures for multimodal data and subsequently advanced next-generation AI through neuro-inspired computational frameworks.
The researcher pioneered the application of multilayer graph transformer networks to reading checks, establishing a foundational framework later expanded into broader geometric deep learning paradigms.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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