Florian Méhats

Florian Méhats

Professor in Applied Mathematics

Ravel Technologies, on leave from the University of Rennes

Biography

I am currently a research associate in the startup Ravel Technologies, on leave from my position of Professor in applied mathematics in the University of Rennes. My research interests are homomorphic cryptography, machine learning/AI, mathematical and numerical modeling of physics (modeling of interacting particles, with applications to plasma physics, nanoelectronics and astrophysics).

Interests
  • Homomorphic cryptography
  • Machine learning and deep learning
  • Mathematical and numerical modeling
  • Partial Differential Equations (kinetic equations)
  • Quantum physics, plasma physics, kinetic models for gravitation
Education

  • Habilitation degree, 2004

    University of Toulouse 3

  • PhD in applied mathematics, 1994-1997

    CMAP, Ecole polytechnique

  • Engineer, 1990-1993

    Ecole polytechnique

Experience

 
 
 
 
 
Senior researcher in homomorphic cryptography
Ravel Technologies
October 2021 – Present
 
 
 
 
 
Professor in Applied Mathematics
University of Rennes 1
September 2005 – Present
 
 
 
 
 
Director of Mathematics Institute
IRMAR (Institut de Recherche Mathématique de Rennes)
January 2015 – August 2017
 
 
 
 
 
Head of Numerical Analysis team
IRMAR (Institut de Recherche Mathématique de Rennes)
September 2006 – August 2015
 
 
 
 
 
In charge of the Magistère de Mathématiques de Rennes
University of Rennes 1
September 2006 – August 2015
 
 
 
 
 
Assistant Professor in Applied Mathematics
University of Toulouse 3
September 1999 – August 2005
 
 
 
 
 
Engineer in integration of cryptography in electronic systems
DGA/CELAR
January 1998 – August 1999

Recent Publications

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Philippe Chartier, Michel Koskas, Mohammed Lemou, Florian Méhats (2024). Homomorphic sign evaluation using functional bootstrapping with a RNS representation of integers.

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Philippe Chartier, Michel Koskas, Mohammed Lemou, Florian Méhats (2024). Fully Homomorphic Encryption on large integers.

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Samuel Guilluy, Florian Méhats, Billal Chouli (2024). Boolean Ring of Blackening Operators and applications to Representation Learning.

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Marco David, Florian Méhats (2023). Symplectic learning for Hamiltonian neural networks. Journal of Computational Physics.

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Maxime Bouchereau, Philippe Chartier, Mohammed Lemou, Florian Méhats (2023). Machine Learning Methods for Autonomous Ordinary Differential Equations. arXiv preprint arXiv:2304.09036.

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