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).
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