PeopleofSuperintelligence

Researcher

Florent Ségonne

Florent Ségonne is a researcher whose work applies topology-controlled segmentation to medical images. He studied theoretical physics at École Polytechnique and applied mathematics at École Nationale des Ponts et Chaussées, then earned an MIT master's while beginning doctoral research on medical-image segmentation. At INRIA's Odyssee project, he built open-source tools to segment brain structures and coauthored papers on MRI skull stripping, level sets and image transduction. His 2015 paper studied spot and implied volatility; his 2024 work examined the S&P 500's at-the-money skew; his 2025 paper proposed an isotropy-based framework for portfolio optimization.

Works
49
Citations
19,734
h-index
17

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 1996–2002

    Theoretical physics and applied math

    He studies theoretical physics at École Polytechnique from 1996 to 1999, then applied mathematics at École Nationale des Ponts et Chaussées from 1999 to 2002.

  2. 2001–2002

    MIT master's and doctoral research

    At MIT, he earns a master's in electrical engineering and computer science in 2002 and begins doctoral research on medical-image segmentation under the supervision of Bruce Fischl and Eric Grimson.

  3. 2003

    An INRIA research fellowship

    In 2003, he joins INRIA's Odyssee project as a visiting research fellow and implements open-source software to segment multiple brain structures under topological constraints.

  4. 2004

    Hybrid MRI skull stripping

    In 2004, he coauthors the NeuroImage paper “A Hybrid Approach to the Skull Stripping Problem in MRI.”

  5. 2006–2008

    Level sets and image transduction

    In 2006, he coauthors work on level-set methods for point correspondence, topological constraints and deformation priors. In 2008, he coauthors the CVPR paper “Segmentation by transduction.”

  6. 2015

    Variance dynamics in finance

    In 2015, he studies the joint dynamics of spot and implied volatility in equities, using the S&P 500 as the underlying.

  7. 2024

    S&P 500 skew and Quant Basics

    In 2024, he coauthors a preprint on the S&P 500's at-the-money skew.

  8. 2025

    Basis Immunity

    In 2025, he proposes a portfolio-optimization framework that uses an isotropy constraint to regularize uncertainty in the signal.

Career

  1. 2008–2011Université Paris-Est Marne-la-Vallée
  2. 2007–2009Institut des Sciences de l'Information et de leurs Interactions
  3. 2005–2007MIT CSAIL
  4. 2004–2009École des Ponts ParisTech
  5. 2003InriaVisiting research fellow

Education

  1. 2002École des Ponts ParisTechApplied mathematics
  2. 2002Massachusetts Institute of TechnologyMaster's
  3. From 2002Massachusetts Institute of TechnologyDoctoral research
  4. 1999École PolytechniqueTheoretical physics

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Massachusetts Institute of Technology. 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.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Massachusetts Institute of Technology. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. Google had the most people shaping AI every year from 2014 to 2025Includes Massachusetts Institute of Technology. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  4. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Massachusetts Institute of Technology. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Florent Segonne people.csail.mit.edu. Checked 21 October 2026.
  2. Mathematics and Image Analysis 2006 ceremade.dauphine.fr. Checked 21 October 2026.
  3. CVPR 2008 iacl.ece.jhu.edu. Checked 21 October 2026.
  4. Variance Dynamics - An empirical journey arxiv.org. Checked 21 October 2026.
  5. Publications | SIMPAS Team | CMAP – École Polytechnique simpas-cmap.github.io. Checked 21 October 2026.
  6. Basis Immunity: Isotropy as a Regularizer for Uncertainty arxiv.org. Checked 21 October 2026.