PeopleofSuperintelligence

Researcher

Gabriel Peyré

Gabriel Peyré is an applied mathematician whose work connects optimal transport with machine learning and AI. He won the Gamni-Smai Blaise Pascal Prize in 2017, co-authored Computational Optimal Transport in 2019, and received the CNRS Silver Medal in 2021. After promotion to research director at CNRS, he joined ENS’s Department of Mathematics and Applications, where his interests shifted toward AI mathematics, and he has directed its Center for Data Science since 2022. With Takashi Furuya and Maarten V. de Hoop, he co-authored a 2024 paper on transformer in-context learning; by 2025, ERC funding backed his WOLF project to bring optimal-transport tools into open-source libraries for computational genomics. As of October 2026, he is scientific director of normalsup.ai and directs ENS’s Center for Data Science.

Now at
École Normale Supérieure and normalsup.ai
Works
457
Citations
17,669
h-index
58

Publication numbers from OpenAlex, as of 15 October 2026.

Timeline

  1. 2017

    The Blaise Pascal Prize

    He receives the Gamni-Smai Blaise Pascal Prize from the Académie des sciences in 2017 for work on sparse image analysis, numerical optimal transport, nonsmooth optimization, and inverse-problem regularization.

  2. 2019

    Computational optimal transport

    With Marco Cuturi, he publishes Computational Optimal Transport in Foundations and Trends in Machine Learning in 2019. The book reviews numerical methods for optimal transport and their applications to data science.

  3. 2021

    A CNRS silver medal

    He receives the CNRS Silver Medal in 2021 as one of six ENS researchers. His work links applied mathematics, imaging, machine learning, and optimal transport.

  4. Since 2022

    AI mathematics at ENS

    After becoming a CNRS research director, he joins ENS’s Department of Mathematics and Applications, where his interests turn toward AI mathematics. He uses and develops tools based on optimal transport. Since 2022, he directs ENS’s Center for Data Science.

  5. 2024

    Transformers are universal in-context learners

    With Takashi Furuya and Maarten V. de Hoop, he posts Transformers are Universal In-context Learners to arXiv in 2024. The paper shows that deep transformers can approximate continuous in-context mappings.

  6. By 2025

    The WOLF multi-omics project

    By 2025, an ERC Advanced Grant backs his WOLF project, which aims to bring optimal-transport tools into open-source deep-learning libraries for computational genomics.

  7. As of October 2026

    Directing normalsup.ai

    As of October 2026, he is scientific director of normalsup.ai and directs the ENS Center for Data Science.

Sources and credits

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  1. Grands Prix de l'Académie des Sciences | ENS ens.psl.eu. Checked 14 October 2026.
  2. Computational Optimal Transport | Google Research research.google. Checked 14 October 2026.
  3. Médailles de bronze et d'argent du CNRS 2021 : des résultats exceptionnels pour l'ENS-PSL | ENS ens.psl.eu. Checked 14 October 2026.
  4. « Nous vivons un moment charnière à la fois pour les mathématiques et pour l’IA » | ENS ens.psl.eu. Checked 14 October 2026.
  5. Transformers are Universal In-context Learners arxiv.org. Checked 14 October 2026.
  6. Gabriel Peyré nommé directeur scientifique de normalsup.ai, le pôle IA de l’ENS-PSL – Département de mathématiques et applications math.ens.psl.eu. Checked 14 October 2026.
  7. “Il y a une révolution scientifique” : la rue d'Ulm se confronte à l’IA | Alliancy alliancy.fr. Checked 14 October 2026.