PeopleofSuperintelligence

Researcher

Elvis Dohmatob

Elvis Dohmatob is an AI researcher whose work spans machine-learning theory, robustness, and synthetic-data training. He completed a computer-science PhD at the University of Paris-Saclay in 2017 and held a postdoctoral position under Bertrand Thirion. In 2018, he moved into industry research; before joining Meta in 2021, he spent three years in a similar research position at Criteo. His ICML 2019 paper analyzed adversarial robustness, and his work at NeurIPS 2024 and ICLR 2025 examined model collapse and verification for synthetic data. At Concordia, he is an associate professor in Computer Science and Software Engineering and affiliate faculty at the Mila Institute.

Born
1987
Badges

Years link to their sources.

Timeline

  1. 2017

    A Paris-Saclay PhD

    In 2017, he earns a computer-science PhD at the University of Paris-Saclay under Bertrand Thirion and Gaël Varoquaux. His thesis examines machine learning, neuroscience and signal processing.

  2. By 2018

    A postdoctoral position

    After the PhD, he completes a postdoctoral position in the same lab under Bertrand Thirion, then moves into industry research in 2018.

  3. 2019

    A robustness theorem

    In 2019, he publishes an ICML paper that presents new impossibility results for adversarial robustness in machine learning.

  4. By 2021

    Research at Criteo

    Before joining Meta in 2021, he spends three years in a similar machine-learning research position at Criteo.

  5. 2023

    Research at Meta FAIR

    In 2023, he works as a senior machine-learning researcher at Meta’s AI lab in Paris, studying theoretical and algorithmic questions of safety, robustness, fairness and trustworthiness.

  6. 2024

    Model collapse in regression

    At NeurIPS 2024, with Yunzhen Feng and Julia Kempe, he analyzes model collapse in high-dimensional Gaussian regression and derives formulas for the phenomenon.

  7. 2025

    Verification for synthetic data

    At ICLR 2025, he coauthors a study that tests verification as a way to prevent model collapse when models train on synthesized data.

  8. As of October 2026

    Concordia and Mila roles

    He joins Concordia as a professor in 2024. As of October 2026, he is an associate professor in the Department of Computer Science and Software Engineering and affiliate faculty at the Mila Institute.

Career

  1. Since 2024Concordia UniversityAssociate Professor
  2. NowMilaAffiliate Faculty
  3. Until 2021CriteoMachine-learning researcher
  4. MetaSenior machine-learning researcher

Education

  1. 2017Université Paris-SaclayPhD

From the biography.

  1. Universities (31%) and startups and other companies (25%) employ the largest shares of 2020–2026 entrantsIncludes Mila. Among 819 people whose first recorded role started in 2020–2026 and who have a known current organization, 60% work in commercial sectors (25% at startups and other companies, 23% at frontier labs and 13% at big tech, with 9 in two), and 31% work at universities, compared with 66% for earlier entrants.
  2. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Mila. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  3. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Mila. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Mila. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Seminar: Fundamental Limits of AI: The Case of Robustness (the lack thereof) | Events - Concordia University concordia.ca. Checked 21 October 2026.
  2. Generalized No Free Lunch Theorem for Adversarial Robustness proceedings.mlr.press. Checked 21 October 2026.
  3. Model Collapse Demystified: The Case of Regression proceedings.neurips.cc. Checked 21 October 2026.
  4. Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification proceedings.iclr.cc. Checked 21 October 2026.
  5. Elvis Dohmatob - Concordia University concordia.ca. Checked 21 October 2026.