Mathieu Laurière

Mathieu Laurière

Mathieu Laurière is an Assistant Professor of Mathematics and Data Science at NYU Shanghai. Prior to joining NYU Shanghai, he was a Postdoctoral Research Associate at Princeton University in the Operations Research and Financial Engineering (ORFE) department. He obtained his MS from Sorbonne University and ENS Paris-Saclay and his PhD from the University of Paris. Before joining Princeton University, he was a Postdoctoral Fellow at the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai. Most recently, Mathieu was a Visiting Faculty Researcher at Google Brain, for the Brain Team (Paris).

Select Publications

  • Carmona, R., and Laurière, M. Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: I - the ergodic case. To appear in SIAM Journal on Numerical Analysis (2021)
  • Carmona, R., Cooney, D., Graves, C., and Laurière, M. Stochastic Graphon Games: I. The Static Case. To appear in Mathematics of Operations Research (2021)
  • Achdou, Y., Laurière, M., and Lions, P.-L. Optimal control of conditioned processes with feedback controls. Journal de Mathématiques Pures et Appliquées (2020)
  • Perrin, S., Pérolat, J., Laurière, M., Geist, M., Elie, R., and Pietquin, O. Fictitious play for mean field games: Continuous time analysis and applications. In 34th Conference on Neural Information Processing Systems, NeurIPS 2020 (2020)
  • Elie, R., Pérolat, J., Laurière, M., Geist, M., and Pietquin, O. On the convergence of model free learning in mean field games. In 34th AAAI Conference on Artificial Intelligence, AAAI 2020


  • PhD, Mathematics and Computer Science
    University of Paris
  • MS, Mathematics
    Sorbonne University
  • MS, Computer Science
    Ecole Normale Supérieure Paris-Saclay


Research Interests

  • Computational Methods
  • Optimal Control
  • Game Theory
  • Partial Differential Equations
  • Stochastic Analysis
  • Deep Learning
  • Reinforcement Learning