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Yann LeCun's Home Page

Yann LeCun,
Chief AI Scientist, Meta
Jacob T. Schwartz Professor of Computer Science, Data Science, Neural Science, and Electrical and Computer Engineering, New York University.
ACM Turing Award Laureate, (sounds like I'm bragging, but a condition of accepting the award is to write this next to your name)
Member, National Academy of Engineering, National Academy of Sciences, Académie des Sciences

last updated: 2024-07-14

Social Networks

Facebook: yann.lecun
Twitter: @ylecun
LinkedIn: yann-lecun
Threads: @yannlecun

Biography / CV

Curriculum Vitae

bios of various lengths in English and en francais

Contact Information

NYU Affiliations:
CILVR Lab (Computational Intelligence, Learning, Vision, Robotics), NYU
Computer Science Department, Courant Institute of Mathematical Sciences, NYU
Center for Data Science, NYU
Center for Neural Science, NYU Faculty of Arts and Sciences
Department of Electrical and Computer Engineering, NYU Tandon School of Engineering

Meta Affiliation:
Meta FAIR (Fundamental AI Research)

Executive Assistant - Meta: Sean Nguyen: sean0[at]meta.com
Administrative Aide - NYU: Hong Tam +1-212-998-3374     hongtam[at]cs.nyu.edu

FOR INVITATIONS TO SPEAK: please send email to lecuninvites[at]gmail.com
(I really can't handle invitations sent to other email addresses)

IF YOU REALLY NEED ME TO DO SOMETHING FOR YOU: (e.g. a review, a letter...) please send email to Sean Nguyen sean0[at]meta.com

NYU coordinates:
Address: Room 516, 60 Fifth Avenue, New York, NY 10011, USA.
Email: yann.lecun[at]nyu.edu (I may not respond right away)
Phone: +1-212-998-3283 (I am very unlikely to respond or listen to voice mail in a timely manner)

Meta Coordinates:
Address: 380 W 33rd St, New York, NY 10001
Email: yann[at]meta.com (I may not respond right away)

Publications, Talks, Courses, Videos, Podcasts, Interviews

Google Scholar
Papers on OpenReview.net
Preprints on ArXiv Out of date list of publications with PDFs and DjVu

Talks / Slide Decks:
Slides of (most of my) talks

Deep Learning Course:
Deep Learning course at NYU:
Complete course on Deep Learning, with all the material available on line including lecture and practicum videos, slide decks, homeworks, Jupyter notebooks, and transcripts in several languages.

Videos: Playlists on YouTube:

Podcasts in English:

  • Lex Friedman #416, 03/2024 YouTube "Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI"
  • Twenty Minute VC with Harry Stebbing, 05/2023 Podcast "Yann LeCun on Why Artificial Intelligence Will Not Dominate Humanity..."
  • With Andrew Ng, 04/2023 YouTube "Yann LeCun and Andrew Ng: Why the 6-month AI Pause is a Bad Idea"
  • Big Technology Podcast with Alex Kantrowitz, 01/2023 YouTube "Is ChatGPT A Step Toward Human-Level AI?"
  • Boz to the Future with Andrew Bosworth, 08/2022 Apple Podcasts
  • Eye on AI with Craig Smith #150 podcast "World Models, AI Threats and Open Sourcing"
  • Lex Friedman #258, 01/2022 YouTube "Dark Matter of Intelligence and Self-Supervised Learning"
  • Big Technology Podcast with Alex Kantrowitz, 12/2021 YouTube "Daniel Kahneman and Yann LeCun: How To Get AI To Think Like Humans"
  • The Robot Brains Podcast with Pieter Abbeel, 09/2021 YouTube "Yann LeCun explains why Facebook would crumble without AI"
  • The Gradient Podcast, 08/2021 The Gradient "Yann LeCun on his Start in Research and Self-Supervised Learning"
  • TED with Chris Anderson, 06/2020 Video "Deep learning, neural networks and the future of AI"
  • Lex Friedman #36, 08/2019 YouTube "Deep Learning, ConvNets, and Self-Supervised Learning"
  • Eye on AI with Craig Smith #114 podcast "Filling the gap in LLMs"
  • Eye on AI with Craig Smith #017, 06/2019 video,podcast

Podcasts en français:

  • Generation DIY #397 avec Matthieu Stefani 06/2024 podcast "L’Intelligence Artificielle Générale ne viendra pas de Chat GPT"
  • Toutes mes interviews sur France Inter playlist
  • Interview sur Europe1 06/2023 podcast "Yann LeCun : «L'intelligence artificielle va amplifier l'intelligence humaine»"

Main Research Interests:
AI, Machine Learning, Computer Vision, Robotics, and Computational Neuroscience. I am also interested Physics of Computation, and many applications of machine learning.

Working Paper

A Path Towards Autonomous Machine Intelligence

(June 2022)

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.


Quand La Machine Apprend

La revolution des neurones artificiels et de l'apprentissage profond (Editions Odile Jacob, Octobre 2019)
Exists in Chinese, Japanese, and Russian.

La Plus Belle Histoire de l'Intelligence

Des origines aux neurones artificiels : vers une nouvelle étape de l'évolution
Stanislas Dehaene, Yann Le Cun, Jacques Girardon (Éditions Robert Laffont, Octobre 2018)

Pamphlets and opinions

Proposal for a new publishing model in Computer Science

Many computer Science researchers are complaining that our emphasis on highly selective conference publications, and our double-blind reviewing system stifles innovation and slow the rate of progress of Science and technology.

This pamphlet proposes a new publishing model based on an open repository and open (but anonymous) reviews which creates a "market" between papers and reviewing entities.


Students and Postdocs

Current PhD Students

  • Gaoyue ''Kathy'' Zhou (NYU CS with Lerrel Pinto) [SSL for control and planning]
  • Peter Tong (NYU CS with Saining Xie) [SSL for video]
  • Kevin Zhang (NYU CS) [SSL for planning]
  • Megi Dervishi (FAIR-Université Paris-Dauphine with Alexandre Allauzen) [SSL for text]
  • Quentin Garrido (FAIR-Université Gustave Eiffel with Laurent Najman) [SSL for images and video]
  • Vlad Sobal (NYU CDS) [SSL for planning and control]

Current Postdocs

  • Amir Bar (FAIR) [SSL for video]
  • Ravid Schwartz-Ziv (NYU) [SSL and information theory]
  • Gelareh Naseri (NYU) [music synthesis and composition]

Former PhD Students

  • Katrina Drozdov Evtimova (2024 NYU CDS) [latent variable JEPA]
  • Adrien Bardes (2024 FAIR-INRIA with Jean Ponce) [SSL, VICReg, I-JEPA, V-JEPA]. FAIR
  • Zeming Lin (2023 NYU CS) [Transformers for protein structure]. FAIR, EvolutionaryScale AI
  • Aishwarya Kamath (2023 NYU CDS) [vision-language models] DeepMind
  • Junbo ``Jake'' Zhao (2019 NYU CS) [energy-based models] faculty Zhejiang University
  • Xiang Zhang (2018 NYU CS) [deep learning for NLP] Element AI, Google AI, startup
  • Mikael Henaff (2018 NYU CS) [deep learning for control] Microsoft Research, FAIR
  • Remi Denton (2018 NYU CS, with Rob Fergus) [video prediction] Google
  • Sainbayar Sukhbaatar (2018, NYU CS with Rob Fergus) [memory, intrinsic motivation, multiagent communication] FAIR
  • Michael Mathieu (2017 NYU CS) [DL for video prediction and image understanding] DeepMind
  • Jure Zbontar (2016 U. of Ljubljana, co-advised) [DL for stereo vision] NYU, FAIR, OpenAI
  • Sixin Zhang (2016 NYU CS) [paralellized deep learning] ENS-Paris, faculty Institut National Polytechnique de Toulouse
  • Wojciech Zaremba (2016 NYU CS with Rob Fergus) [algorithm synthesis] OpenAI
  • Rotislav Goroshin (2015 NYU CS) [unsupervised representation learning] DeepMind
  • Pierre Sermanet (2014 NYU CS) [DL for vision and mobile robot perception] Google Brain, DeepMind
  • Clément Farabet (2014 U. Gustave Eiffel with Laurent Najman) [dedicated hardware for ConvNets, vision, Torch-7] Twitter, Nvidia, VP of Research DeepMind
  • Fu Jie Huang (2013 NYU CS) [DL for vision] Milabra, Kanerai
  • Kevin Jarrett (2012 NYU Neural Science) [DL models of biological vision] Bridgewater,...,Barclays
  • Matthew Grimes (2012 NYU) [SLAM] Cambridge, DeepMind
  • Y-Lan Boureau (2012, NYU-INRIA with Jean Ponce) [sparse feature learning for vision] Flatiron Institute, FAIR, CEO ThrivePal)
  • Koray Kavakcuoglu (2010, NYU) [sparse auto-encoders for unsupervised feature learning] NEC Labs, VP or Research DeepMind
  • Piotr Mirowski (2010 NYU) Bell Labs, Microsoft, DeepMind
  • Ayse Erkan (2010 NYU, with Yasemine Altun) Twitter, Robinhood, CEO Laminar AI.
  • Marc'Aurelio Ranzato (2009 NYU) Google X-Labs, FAIR, DeepMind.
  • Sumit Chopra (2008 NYU) AT&T Labs-Research, FAIR, Imagen, faculty NYU.
  • Raia Hadsell (2008 NYU) SRI, VP DeepMind
  • Feng Ning (2006 NYU) Bank of America, Société Générale, ScotiaBank, AQR Capital, VP AllianceBernstein.

Former Postdocs

  • Micah Goldblum (NYU 2021-2024), Columbia University
  • Grégoire Mialon (FAIR 2021-2023), Meta-GenAI
  • Randall Balestriero (FAIR 2021-2023), Brown University
  • Nicolas Carion (NYU 2020-2022), FAIR
  • Yubei Chen (FAIR 2020-2022), UC Davis
  • Li Jing (FAIR 2019-2021), OpenAI
  • Jacob Browning (NYU 2019-2023): philosophy and history of AI (Berggruen Transformation of the Human program)
  • Phillip Schmitt (NYU 2019-2021): AI and the visual arts (Berggruen Transformation of the Human program)
  • Stéphane Deny (FAIR 2019-2021), U of Aalto
  • Alfredo Canziani (NYU 2017-2022), NYU: autonomous driving, AI education
  • Behnam Neyshabur (NYU 20172019), Google, DeepMind: deep learning landscape, self-supervised learning
  • Jure Zbontar (NYU 2016-2017). FAIR, OpenAI: temporal prediction
  • Anna Choromanska (NYU 2014-2017) NYU Tandon: applied mathematics
  • Pablo Sprechmann (NYU 2014-2017), DeepMind: applied mathematics and signal processing
  • Joan Bruna (NYU 2012-2014), FAIR, UC Berkeley, NYU: applied mathematics
  • Camille Couprie (NYU 2011-2013), FAIR: computer vision
  • Tom Schaul (NYU 2011-2013), DeepMind: machine learning and optimization
  • Jason Rolfe (NYU 2011-2013), D-Wave, Variational AI: computational neuroscience
  • Leo Szhu (NYU 2010-2011), startup: hierarchical vision models.
  • Arthur Szlam (NYU 2009-2011), CUNY, FAIR, DeepMind: applied mathematics.
  • Karol Gregor (NYU 2008-2011), Janelia Farm, DeepMind: machine learning.
  • Trivikraman Thampy (NYU 2008-2009), startup: financial modeling and prediction.
  • Joseph Turian (NYU 2007-2007), MetaOptimize: energy-based models.
Bragging Zone

Honors and Awards

  • Professor Honoris Causa, ESIEE / Université Gustave Eiffel, 2024
  • Lifetime Honorary Membership, New York Academy of Sciences, 2024
  • Fellow Association for Computing Machinery, 2024
  • Great Immigrant, Carnegie Corporation of New York, 2024
  • TIME 100 Impact Award, 2024
  • Membre Honoraire, Société Informatique de France, 2024
  • Chevalier de la Légion d'Honneur, France, 2023
  • Global Swiss AI Award for outstanding global impact in the field of artificial intelligence, 2023
  • Inaugural Professorship, Jacob T. Schwartz Chair in Computer Science, Courant Institute, NYU. 2023
  • Doctorate Honoris Causa, Hong Kong University of Science and Technology, 2023
  • Doctorate Honoris Causa, Università di Siena, 2023
  • Princess of Asturias Award, for Technical and Scientific Research (with Demis Hassibis, Yoshua Bengio, and Geoffrey Hinton), 2022
  • Foreign Member, Académie des Sciences, France, 2022
  • Fellow, American Association for the Advancement of Science, 2021
  • Member, US National Academy of Sciences, 2021
  • Doctorate Honoris Causa, Université Côte d'Azur, 2021
  • Fellow, Association for the Advancement of Artificial Intelligence, 2020
  • Golden Plate Award, International Academy of Achievement, 2019
  • ACM A.M. Turing Award, 2018 (shared with Geoffrey Hinton and Yoshua Bengio)
  • Doctorate Honoris Causa, Ecole Polytechnique Fédérale de Lausanne, 2018
  • Holst Medal, Technical University of Eindhoven \& Philips Labs, The Netherlands
  • Pender Award, University of Pennsylvania, 2018
  • Member, US National Academy of Engineering, Class of 2017.
  • Nokia-Bell Labs Shannon Luminary Award, 2017
  • Annual Chair in Computer Science, Collège de France 2015-2016.
  • Lovie Lifetime Achievement Award, International Academy of Digital Arts and Sciences, 2016.
  • Inductee, New Jersey Inventor Hall of Fame, 2016.
  • Doctorate Honoris Causa, Instituto Politécnico Nacional, Mexico, 2016.
  • IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award, 2015.
  • IEEE Neural Network Pioneer Award, 2014.
  • NYU Silver Professorship, 2008.
  • Fyssen Foundation Fellowship, 1987.

In the Media

[stuff below this line is badly out of date]

Quick Links

Computational and Biological Learning Lab

My lab at the Courant Institute of New york University is called the Computational and Biological Learning Lab.

See research projects descriptions, lab member pages, events, demos, datasets...

We are working on a class of learning systems called Energy-Based Models, and Deep Belief Networks. We are also working on convolutional nets for visual recognition , and a type of graphical models known as factor graphs.

We have projects in computer vision, object detection, object recognition, mobile robotics, bio-informatics, biological image analysis, medical signal processing, signal processing, and financial prediction,....


Jump to my course page at NYU, and see course descriptions, slides, course material...

Talks and Tutorials

See, watch and hear talks and tutorial.

Deep Learning

Animals and humans can learn to see, perceive, act, and communicate with an efficiency that no Machine Learning method can approach. The brains of humans and animals are "deep", in the sense that each action is the result of a long chain of synaptic communications (many layers of processing). We are currently researching efficient learning algorithms for such "deep architectures". We are currently concentrating on unsupervised learning algorithms that can be used to produce deep hierarchies of features for visual recognition. We surmise that understanding deep learning will not only enable us to build more intelligent machines, but will also help us understand human intelligence and the mechanisms of human learning.


Relational Regression

We are developing a new type of relational graphical models that can be applied to "structured regression problem". A prime example of structured regression problem is the prediction of house prices. The price of a house depends not only on the characteristics of the house, but also of the prices of similar houses in the neighborhood, or perhaps on hidden features of the neighborhood that influence them. Our relational regression model infers a hidden "desirability sruface" from which house prices are predicted.

Mobile Robotics

The purpose of the LAGR project, funded by the US government, is to design vision and learning algorithms to allow mobile robots to navigate in complex outdoors environment solely from camera input.

My Lab, collaboration with Net-Scale Technologies is one of 8 participants in the program (Applied Perception Inc., Georgia Tech, JPL, NIST, NYU/Net-Scale, SRI, U. Penn, Stanford).

Each LAGR team received identical copies of the LAGR robot, built be the CMU/NREC.

The government periodically runs competitions between the teams. The software from each team is loaded and run by the goverment team on their robot.

The robot is given the GPS coordinates of a goal to which it must drive as fast as possible. The terrain is unknown in advance. The robot is run three times through the test course.

The software can use the knowledge acquired during the early runs to improve the performance on the latter runs.


Prior to the LAGR project, we worked on the DAVE project, an attempt to train a small mobile robot to drive autonomously in off-road environments by looking over the shoulder of a human operator.


Energy-Based Models

Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discriminative and generative approaches, as well as graph-transformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods.

Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches.


Invariant Object Recognition


The recognition of generic object categories with invariance to pose, lighting, diverse backgrounds, and the presence of clutter is one of the major challenges of Computer Vision.

I am developing learning systems that can recognize generic object purely from their shape, independently of pose and lighting.


The NORB dataset for generic object recognition is available for download.


Lush: A Programming Language for Research

Tired of Matlab? Lush is an easy-to-learn, open-source object-oriented programming language designed for researchers, experimenters, and engineers working in large-scale numerical and graphic applications.

Lush combines three languages in one: a very simple to use, loosely-typed interpreted language, a strongly-typed compiled language with the same syntax, and the C language, which can be freely mixed with the other languages within a single source file, and even within a single function.

Lush has a library of over 14,000 functions and classes, some of which are simple interfaces to popular libraries: vector/matrix/tensor algebra, linear algebra (LAPACK, BLAS), numerical function (GSL), 2D and 3D graphics (X, SDL, OpenGL, OpenRM, PostScipt), image processing, computer vision (OpenCV), machine learning (gblearning, Torch), regular expressions, audio processing (ALSA), and video grabbing (Video4linux).

If you do research and development in signal processing, image processing, machine learning, computer vision, bio-informatics, data mining, statistics, or artificial intelligence, and feel limited by Matlab and other existing tools, Lush is for you. If you want a simple environment to experiment with graphics, video, and sound, Lush is for you. Lush is Free Software (GPL) and runs under GNU/Linux, Solaris, and Irix.


DjVu: The Document Format for Digital Libraries

My main research topic until I left AT&T was the DjVu project. DjVu is a document format, a set of compression methods and a software platform for distributing scanned and digitally produced documents on the Web. DjVu image files of scanned documents are typically 3-8 times smaller than PDF or TIFF-groupIV for bitonal and 5-10 times smaller than PDF or JPEG for color (at 300 DPI). DjVu versions of digitally produced documents are more compact and render much faster than the PDF or PostScript versions.

Hundreds of websites around the world are using DjVu for Web-based and CDROM-based document repositories and digital libraries.

Learning and Visual Perception

My main research interest is machine learning, particularly how it applies to perception, and more particularly to visual perception.

I am currently working on two architectures for gradient-based perceptual learning: graph transformer networks and convolutional networks.

Convolutional Nets are a special kind of neural net architecture designed to recognize images directly from pixel data. Convolutional Nets can be trained to detect, segment and recognize objects with excellent robustness to noise, and variations of position, scale, angle, and shape.

Have a look at the animated demonstrations of LeNet-5, a Convolutional Nets trained to recognize handwritten digit strings.

Convolutional nets and graph transformer networks are embedded in several high speed scanners used by banks to read checks. A system I helped develop reads an estimated 10 percent of all the checks written in the US.

Check out this page, and/or read this paper to learn more about Convolutional Nets and graph transformer networks.

MNIST Handwritten Digit Database

The MNIST database contains 60,000 training samples and 10,000 test samples of size-normalized handwritten digits. This database was derived from the original NIST databases.

MNIST is widely used by researchers as a benchmark for testing pattern recognition methods, and by students for class projects in pattern recognition, machine learning, and statistics.

Music and Hobbies

I have several interests beside my family (my wife and three sons) and my research:

  • Playing Music: particularly Jazz, Renaissance and Baroque music. A few MP3 and MIDI files of Renaissance music are available here.
  • Building and flying miniature flying contraptions: preferably battery powered, radio controled, and unconventional in their design.
  • Building robots: particularly Lego robots (before the days of the Lego Mindstorms)
  • Hacking various computing equipment: I have owned 5 computers between 1978 and 1992: SYM-1, OSI C2-4P, Commodore 64, Amiga 1000, Amiga 4000. then I lost interest in personal computing when the only thing you could get was a boring Wintel box. Then, Linux appeared and I came back to life.....
  • Sailing: I own two sport catamarans, a Nacra 5.8 and a Prindle 19. I also sail and race larger boats with friends.
  • Graphic Design: I designed the DjVu logo and much of the AT&T DjVu web site.
  • Reading European comics. Comics in certain European countries (France, Belgium, Italy, Spain) are considered a true art form ("le 8-ieme art"), and not just a business with products targeted at teenagers like on this side of the pond. Although I don't have a shred of evidence to support it, I claim to have the largest private collection of French-language comics in the Eastern US.
  • making bad puns in French, but I don't have much of an audience this side of the pond.
  • Sipping wine, particularly red, particularly French, particularly Bordeaux, particularly Saint-Julien.

Bib2Web: Automatic Creation of Publication Pages

Bib2Web No deep science here, but if you are looking for a simple/automatic way to make all your publications (digital or paper-based) available on your web page, visit Bib2Web.

Photos Galleries

Fun Stuff

Previous Life

My former group at AT&T (the Image Processing Research Department) and its ancestor (Larry Jackel's Adaptive Systems Research Department) made numerous contributions to Machine Learning, Image Compression, Pattern Recognition, Synthetic Persons (talking heads), and Neural-Net Hardware. Specific contributions not mentioned elsewhere on this site include the ever so popular Support Vector Machine, the PlayMail and Virt2Elle synthetic talking heads, the Net32K and ANNA neural net chips, and many others. Visit my former group's home page for more details.


Links to interesting places on the web, friends' home pages, etc .


Yann LeCun, Professor
The Courant Institute of Mathematical Sciences

Copyright © 2000-2018 Yann LeCun.

Yann LeCun, Le Cun, deep learning, ConvNet, CNN, LeNet, DjVu, convolutional neural networks, machine learning, computer vision, pattern recognition, document imaging, image compression, digital libraries,