Mathieu Laurière
· Assistant Professor of Mathematics and Data Science and Finance and Risk Engineering, NYU Tandon; Assistant Professor of Mathematics and Data Science, NYU ShanghaiNew York University · Finance and Risk Engineering
Active 2013–2026
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About
Mathieu Laurière is an Assistant Professor of Mathematics and Data Science at NYU Shanghai. His research focuses on mean field control and mean field games, numerical methods, partial differential equations, stochastic analysis, and machine learning. He has a background in mathematics and computer science, holding a PhD from the University of Paris, and master's degrees from Sorbonne University and ENS Paris-Saclay. Prior to his current position, Laurière was a Postdoctoral Research Associate at Princeton University in the Operations Research and Financial Engineering department. He also served as a Postdoctoral Fellow at the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai and was a Visiting Faculty Researcher at Google Brain for the Brain Team in Paris. His work includes contributions to the convergence analysis of machine learning algorithms for mean field control and games, stochastic graphon games, and optimal control of conditioned processes, among others.
Research topics
- Computer Science
- Artificial Intelligence
- Mathematical optimization
- Mathematics
- Economics
- Applied mathematics
- Physics
- Mathematical economics
- Mathematical analysis
- Microeconomics
Selected publications
HAL (Le Centre pour la Communication Scientifique Directe) · 2020-01-01 · 60 citations
articleOpen accessInternational audience
A Machine Learning Method for Stackelberg Mean Field Games
Mathematics of Operations Research · 2024-12-02 · 8 citations
articleSenior authorWe propose a single-level numerical approach to solve Stackelberg mean field game (MFG) problems. In the Stackelberg MFG, an infinite population of agents plays a noncooperative game and chooses their controls to optimize their individual objectives while interacting with the principal and other agents through the population distribution. The principal can influence the mean field Nash equilibrium at the population level through policies, and she optimizes her own objective, which depends on the…
Learning Equilibria in Cournot Mean Field Games of Controls
SIAM Journal on Control and Optimization · 2025-04-25 · 2 citations
articleEuropean Journal of Operational Research · 2025-05-02 · 1 citations
article1st authorAn overview of some extensions of mean field games beyond perfect homogeneity and anonymity
Science China Information Sciences · 2025-11-01 · 1 citations
articleOpen access1st authorCorresponding
Frequent coauthors
- 32 shared
Iordanis Kerenidis
Institut de Recherche en Informatique Fondamentale
- 32 shared
Romuald Élie
- 32 shared
René Carmona
- 26 shared
Olivier Pietquin
- 23 shared
Matthieu Geist
- 20 shared
Sophie Laplante
- 19 shared
Gökçe Dayanıklı
- 19 shared
Yves Achdou
Laboratoire Jacques-Louis Lions
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