All people
Yann LeCun portrait

Researcher · Academic · Founder · Executive

Yann LeCun

AMI LabsCo-founder and Executive ChairmanNew York UniversitySilver Professor of Computer Science

🇫🇷 French-AmericanBorn 1960

LeCun pioneered convolutional neural networks, which recognise patterns in images. His LeNet model powered a commercial system that read millions of bank checks.[1] A shared 2018 Turing Award recipient, he advocates world models that learn how their environment works from observation, aiming to help AI reason and plan beyond predicting text.

Career & education

Current roles
Dec 2025-present
Co-founder and Executive Chairman
2003-present
Silver Professor of Computer Science
Previously
2013-2025
Vice President and Chief AI Scientist
2002-2003
1988-2002
1987-1988
Postdoc
Education

Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.

Citation Trend

Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
11 snapshots
Citation history from Aug 25, '25 to Oct 1, '26499,649460,561421,472Aug 252025Apr 152026Jul 12026Oct 12026
citations499,649As of Oct 1, '26
citations+6,074Since last snapshot, Sep 1, '26
For today’s citation count and per work, visit Google Scholar.

Selected contributions

  • Paper2015

    Deep learning

    With Yoshua Bengio and Geoffrey Hinton

    Reviewed how neural networks learn increasingly complex patterns through multiple layers, explaining the methods behind major advances in image recognition, speech processing, and language understanding.

    Read paper
  • Paper1998

    Gradient-Based Learning Applied to Document Recognition

    With Léon Bottou, Yoshua Bengio and Patrick Haffner

    Showed how neural networks could learn to recognize handwritten characters from images, supporting practical document-reading systems, including technology used to process millions of bank cheques.

    Read paper
  • Paper1989

    Backpropagation Applied to Handwritten Zip Code Recognition

    With B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel

    Demonstrated a neural network that learned to recognize handwritten postal-code digits directly from images, using examples from the U.S. Postal Service rather than relying on manually designed image features.

    Read paper

References

  1. LeCun et al., 1998 — LeNet and check recognitiongwern.net

    LeNet-5, jointly trained recognition systems, and commercial use reading millions of checks. Original paper; PDF mirror.

About this profile

Based on public sources. If accuracy is important, verify details using the links or references.

For corrections, updates, or requests to remove information, contact us.

Reviewed August 8, 2026 · Checked for accuracy and new information.

Sharing card

Related people