Quoc Le
Also known as Quoc V Le
Quoc Le is a researcher whose papers connect learning from unlabeled data with language generation and model design. He studied at the Australian National University and Stanford, where he worked with Andrew Ng. His shared work includes large-scale image feature learning, document representations, sequence-to-sequence learning, and Google’s neural translation system. With Barret Zoph, he studied using reinforcement learning to search for neural network designs. He also appears in the large Gemini collaboration published in 2023.
- Works
- 278
- Citations
- 149,364
- h-index
- 118
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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2011–2012
Finding features in unlabeled images
Le co-authored large-scale unsupervised feature learning in 2011–2012.
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May 2014
Representing a whole document
Le and Tomas Mikolov published document representation research in 2014.
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September 2014
Learning to produce a sequence
Le co-authored sequence-to-sequence learning in September 2014.
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September 2016
Translation at Google
Le co-authored Google’s neural translation paper in 2016.
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2016–2017
Searching for model designs
Zoph and Le introduced neural architecture search in 2016.
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December 2023
Part of the Gemini collaboration
Le was a coauthor of the 2023 Gemini report.
Sources and credits
Photo: Quoc Le, Stanford personal page. License: Not stated by source. Source: ai.stanford.edu. Cropped to a square and resized. Ask us to remove or replace this photo.
- Quoc V. Le: personal academic page
- Building high-level features using large scale unsupervised learning
- Distributed Representations of Sentences and Documents
- Sequence to Sequence Learning with Neural Networks
- Google’s Neural Machine Translation System
- Neural Architecture Search with Reinforcement Learning
- Gemini: A Family of Highly Capable Multimodal Models