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Yann LeCun

Yann LeCun is a French computer scientist known for convolutional neural networks, which learn to recognise images directly from pixels. He co-authored the 1998 paper that introduced LeNet-5 and shared the 2018 ACM A.M. Turing Award.

Born 1960

AffiliationsMeta AIBell LabsUniversity of TorontoNew York University

2022 · 2 20042025
Events per year
7Events4Organisations4Works

Overview

Yann LeCun is a French computer scientist, born on 8 July 1960 in Soisy-sous-Montmorency, near Paris.[6] He is best known for convolutional neural networks, which process images by sliding small learned filters across them. He has also led AI research groups in industry and at universities.

Education and early research

LeCun received a Diplôme d'Ingénieur, a French engineering degree, from ESIEE Paris in 1983, and a PhD in computer science from Université Pierre et Marie Curie in 1987.[6] He then spent a year as a postdoctoral researcher under Geoffrey Hinton at the University of Toronto.[6] A postdoctoral researcher is a scholar who continues research for a fixed term after finishing a doctorate.

Main contributions

According to ACM, while at the University of Toronto and Bell Labs he was the first to train a convolutional neural network on images of handwritten digits.[1] The key idea is weight sharing: the same small filter is applied at every position in the image, so it learns to detect a pattern wherever that pattern appears. The 1989 zip-code paper and the 1998 LeNet-5 paper, which also introduced the MNIST dataset, came from this line of work.[3]

A check-reading system built on these networks was deployed commercially and read several million checks per day.[3] ACM also credits him with an early version of backpropagation, methods to speed it up, and, with Léon Bottou, the idea of building learning systems as networks of modules, each trained by backpropagation through automatic differentiation.[1]

Career

He joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, in 1988. In 1996 he became head of the Image Processing Research Department at AT&T Labs-Research, where he also worked on the DjVu document-compression technology.[6]

He joined New York University in 2003, where he is Silver Professor at the Courant Institute and was founding director of the NYU Center for Data Science.[6] As of 2019 he co-directed CIFAR's Learning in Machines and Brains program with Yoshua Bengio.[1]

He joined Facebook in December 2013 as the first director of Facebook AI Research (FAIR), and later became vice president and chief AI scientist.[6] In November 2025 he confirmed that he would leave Meta at the end of 2025 to found a start-up focused on "Advanced Machine Intelligence" (AMI).[4]

Influence and recognition

LeCun shared the 2018 ACM A.M. Turing Award with Geoffrey Hinton and Yoshua Bengio; the award was announced on 27 March 2019.[1][2] His other honours include the 2014 IEEE Neural Network Pioneer Award, membership of the US National Academy of Engineering, the 2022 Princess of Asturias Award, Chevalier of the Legion of Honour (2023) and the 2025 Queen Elizabeth Prize for Engineering.[5][6]

LeCun's work runs from early experiments on handwritten digits to check-reading systems and image recognition. Its central idea, learning features directly from pixels rather than designing them by hand, is one of the foundations of modern computer vision.

CapabilitiesComputer vision

TechniquesDeep learning

HistoryResearch history

Relationships

Contributed to

Convolutional neural networks

Career timeline

9
  1. 1987
    • University of Toronto Worked at 1987–1988

      A public research university in Toronto where Geoffrey Hinton has been affiliated since 1987 and where AlexNet, the 2012 deep convolutional network, was developed.

  2. 1988
    • Bell Labs Worked at 1988–1996

      Bell Labs was the research arm of the Bell System, where the transistor, Unix and the C language were created. Yann LeCun's convolutional networks for handwritten digits were developed there, which made it an early home of neural-network research.

  3. 1989
    • Convolutional neural networks Contributed to

      A convolutional neural network applies the same small pattern detectors across a whole image, so a feature is found wherever it appears. Trained with backpropagation in 1989 and refined as LeNet-5 in 1998, it became standard in computer vision.

    • Backpropagation Applied to Handwritten Zip Code Recognition Contributed to

      In 1989 LeCun and colleagues at AT&T Bell Laboratories trained a convolutional network with backpropagation to read handwritten zip-code digits from U.S. mail, reaching 5.0% test error. The work showed that the convolutional design could be learned end to end on a real task.

  4. 1998
    • MNIST database of handwritten digits Contributed to

      MNIST is a database of 60,000 training and 10,000 test images of handwritten digits, normalized to 28×28 pixels and described in the 1998 paper by LeCun and colleagues as a shared benchmark for document recognition.

    • Gradient-Based Learning Applied to Document Recognition Contributed to

      LeCun, Bottou, Bengio and Haffner’s 1998 review argued that learning systems should replace hand-designed features. It showed that convolutional networks such as LeNet-5 outperformed other methods on handwritten digits, and it introduced the MNIST dataset.

  5. 2003
    • New York University Worked at 2003–

      New York University, chartered in 1831 and centred on Washington Square Park, is a private research university whose Courant Institute and Center for Data Science made it a home for machine learning research and data science training.

  6. 2013
    • Meta AI Worked at 2013–2025

      Meta AI is the AI research and model-building organisation of Meta Platforms. Founded in late 2013 as Facebook AI Research, it produced PyTorch and the Llama and Muse model families, and it is separate from the Meta AI assistant product.

  7. 2015
    • Deep learning Contributed to

      A 2015 Nature review by Yann LeCun, Yoshua Bengio and Geoffrey Hinton explaining deep learning: models of multiple processing layers that learn layered representations of data, and the results this produced in speech, vision and other fields.

Worked at4

Meta AI2013–2025Bell Labs1988–1996University of Toronto1987–1988New York University2003–

20251

  1. DepartureYann LeCun leaves Meta to found an AI startup

    On 19 November 2025 Yann LeCun confirmed that he would leave Meta after ten years to found an AI startup focused on advanced machine intelligence. Meta was to be a partner of the new company.

20222

  1. ReleaseMeta launches Galactica and pulls its demo three days later

    On 15 November 2022 Meta AI and Papers with Code unveiled Galactica, a language model meant to help scientists, with a public demo. Meta took the demo offline about three days later after users showed it producing fabricated, confident answers.

  2. MilestoneHinton, LeCun, Bengio and Hassabis win the 2022 Princess of Asturias Award

    In June 2022, the Princess of Asturias Award for Technical and Scientific Research was awarded jointly to Geoffrey Hinton, Yann LeCun, Yoshua Bengio and Demis Hassabis for advancing artificial intelligence and deep learning.

20191

  1. MilestoneBengio, Hinton and LeCun receive the 2018 ACM Turing Award

    In March 2019 ACM named Yoshua Bengio, Geoffrey Hinton and Yann LeCun the recipients of the 2018 A.M. Turing Award, citing breakthroughs that made deep neural networks a critical component of computing.

20132

  1. MilestoneFacebook founds FAIR with Yann LeCun as director

    In late 2013 Facebook launched Facebook AI Research (FAIR), with Yann LeCun as its first director. The lab's open research model shaped Meta's later AI work, including PyTorch and the Llama models.

  2. MilestoneFirst ICLR held in Scottsdale with open peer review

    ICLR was founded in 2012 by Yann LeCun and Yoshua Bengio, and first met in Scottsdale, Arizona, in May 2013, with paper decisions posted publicly on OpenReview.

20041

  1. MilestoneCIFAR launches the Neural Computation & Adaptive Perception program

    A 2004 CIFAR research program proposed by Geoffrey Hinton with Yoshua Bengio and David Fleet, which CIFAR describes as the starting point of the deep learning era and the predecessor of today's Learning in Machines & Brains program.

Contributed to4

Notes and sources

  1. Fathers of the Deep Learning Revolution Receive ACM A.M. Turing Awardhttps://web.archive.org/web/2024/https://awards.acm.org/about/2018-turing
  2. Turing Award presented to Yann LeCun, Geoffrey Hinton, and Yoshua Bengiohttps://engineering.fb.com/2019/03/27/ai-research/turing-award/
  3. Gradient-Based Learning Applied to Document Recognition (PDF)http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
  4. Confirmed: Yann LeCun leaves Metahttps://heise.de/-11085694
  5. 2025 Queen Elizabeth Prize for Engineering: Modern Machine Learninghttps://qeprize.org/winners/modern-machine-learning
  6. Yann LeCunhttps://en.wikipedia.org/wiki/Yann_LeCun

Cite this page: Yann LeCun. TellWorld, https://www.tellworld.net/people/yann-lecun. Updated 2026-11-07