Yann LeCun

About

Yann LeCun is the Jacob T. Schwartz Chaired Professor of Computer Science at New York University. He received the 2025 Queen Elizabeth Prize for Engineering from King Charles III during a ceremony at St. James Palace in London, recognizing his pioneering contributions to the development of modern machine learning, a field that underpins the rapid advancement of artificial intelligence. His work has significantly impacted the field of artificial intelligence and machine learning, establishing foundational techniques and advancing the state of the art.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Biology
• Natural Language Processing
• Human–computer interaction
• Genetics
• Algorithm
• Cognitive science
• Evolutionary biology

Selected publications

• MDETR - Modulated Detection for End-to-End Multi-Modal Understanding

  2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021 · 660 citations

  Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems to capture the long tail of visual concepts expressed in free form text. In this paper we propose MDETR, an end-to-end modulated detector that detects objects in an image conditione…

  DOI

• The Mind of a Mouse

  Cell · 2020 · 189 citations

  DOI

• A Cookbook of Self-Supervised Learning

  arXiv (Cornell University) · 2023 · 161 citations

  Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, successfully training a SSL method involves a dizzying set of choices from the pretext tasks to training hyper-parameters. Our goal is to lower the barrier to entry into SSL research by laying the foundations and latest SSL recipes in the style of a cookbo…

  DOI

• Decoupled Contrastive Learning

  Lecture notes in computer science · 2022 · 150 citations

  Senior authorCorresponding
  DOI

• Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

  arXiv (Cornell University) · 2022 · 33 citations

  Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Toward Category-Level Object Recognition

  NSF · $255k · 2005–2009

Frequent coauthors

• Pierre Sermanet

  57 shared

• Michaël Mathieu

  44 shared

• Raia Hadsell

  DeepMind (United Kingdom)

  38 shared

• Koray Kavukcuoglu

  38 shared

• Joan Bruna

  New York University

  37 shared

• Clément Farabet

  37 shared

• Y-Lan Boureau

  34 shared

• Léon Bottou

  33 shared

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1988

• M.S., Computer Science

  University of California, Berkeley

  1984

• B.S., Computer Science

  University of Paris VI (Pierre et Marie Curie)

  1980

Awards & honors

• 2025 Queen Elizabeth Prize for Engineering

Similar researchers at New York University

• Hong Wangh-117
• Leif Ristrophh-35
• Jinyoung Parkh-10