Mathieu Besançon

Mathieu Besançon

Researcher in mathematical optimization

Zuse Institute Berlin

Inria Grenoble (incoming)


I am a researcher in mathematical optimization with interests in nonlinear and discrete structures, and an emphasis on formulation and computational aspects. I am currently at the Zuse Institute Berlin, in the IOL group. I am associated with the MODAL-SynLab DFG project and a member of the MATH+ Berlin Mathematics Research Center.
I am joining Inria Grenoble in January 2024 as an Associate Researcher (Chargé de Recherche) and will be working with the POLARIS group within the LIG.

My research interests span the theory, methods, and algorithms for several flavours of mathematical optimization. More specifically, I have been interested in exact solution methods for constrained optimization with constraint structures we can exploit. Those include solution methods, computational models, and software in mixed-integer (non-)linear and convex optimization and in particular around the SCIP framework and Frank-Wolfe related approaches. Recently, I have been exploring applications for these classes of problems in quantum information, systems biology, networks and infrastructure, and data science.

I graduated with a double PhD (cotutelle) between Polytechnique Montréal, at the GERAD lab and Centrale Lille, at INRIA & the Cristal lab, in mathematical optimization. My thesis focused on bilevel optimization, an extension coined near-optimality robustness, and pricing for demand response in smart grids. It was co-supervised by Luce Brotcorne (INRIA) & Miguel F. Anjos (University of Edinburgh).

I am involved in several open-source projects around optimization and scientific computing in the Julia programming language and around JuMP. I worked with and in various industries, from a hardware startup to steel manufacturing. I did my joint Bachelor-Master in Process Engineering at the UTC in France with a semester at the TUBS in Germany and Polytechnique Montreal.

On a personal note, I read both fiction (mostly history, detective, thrillers and fantasy) and non-fiction books (on economic policy, education, transportation systems, the energy transition); a more detailed readling list can be found on my goodread. I also enjoy games in various formats (tabletop, video, board, card) and cooking.

  • Bilevel Optimization
  • Convex Optimization
  • Mixed-Integer (Non-)Linear Optimization
  • Power Systems
  • Mathematical Optimization
  • Optimization and Engineering
  • Optimization Software
  • Algorithm Design
  • Optimization in Statistics & Learning
  • Joint PhD, Applied Mathematics & Computer Science, 2017-2020

    Polytechnique Montreal, INRIA, Centrale Lille, GERAD

  • Joint Bachelor & Master of Science in Process Engineering, 2011-2016

    University of Technology of Compiègne (UTC), France

  • Exchange program, applied mathematics, computer science & industrial engineering, 2015

    Polytechnique Montréal, Canada

  • Exchange semester, Process & Energy Engineering (Bachelor), 2013

    Technische Universität Braunschweig, Germany


Associate Researcher
January 2024 – Present Grenoble, France
Research in optimization.
Postdoctoral Researcher
January 2021 – December 2023 Berlin, Germany
Research in optimization methods and computation.
Doctoral Researcher
Polytechnique Montréal, Inria Lille
September 2017 – December 2020 Montréal, Canada & Lille, France
Double PhD program in mathematical optimization for pricing of Demand Response programs in a smart grid context.
Research Engineer, Data Scientist
Equisense SAS
July 2016 – August 2017 Lille, France
Research and development for a startup building connected devices and associated products for horse-riders.
Master’s Thesis
Siemens AG, Digital Industries
February 2016 – July 2016 Karslruhe, Germany
Stochastic models for event monitoring in automated systems.
Junior Engineer Placement
ArcelorMittal Hamburg GmbH
August 2014 – January 2015 Hamburg, Germany
Quantification and analysis of material losses in a steel rolling mill.


Filter publications here.

(2023). Cutting Plane Selection with Analytic Centers and Multiregression. CPAIOR 2023.


(2023). How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem.

Cite arXiv

(2023). Improved local models and new Bell inequalities via Frank-Wolfe algorithms. arXiv.


(2022). Convex mixed-integer optimization with Frank-Wolfe algorithms.


(2022). Flexible Differentiable Optimization via Model Transformations. In minor revision.



The most reliable way to reach me is per email.