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<p>I am currently an assistant professor of Mathematics and Data Science at NYU Shanghai. I obtained my MS from University Paris 6 and ENS Cachan, and my PhD from University Paris 7. I was a Postdoctoral Fellow at the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai and a Postdoctoral Research Associate at Princeton University, in the Operations Research and Financial Engineering (ORFE) department. Prior to my current position NYU Shanghai, I was a Visiting Faculty Researcher at Google Research, in the Brain Team (Paris).</p>
<p>My research interests include mean field control and mean field games, numerical methods, partial differential equations, stochastic analysis, machine learning, complexity theory and quantum computing. Here is my <a target="_blank" rel="noopener noreferrer" href="https://scholar.google.com/citations?user=6uskEdwAAAAJ">Google scholar</a> page.</p>
<p>I am co-organizing <a target="_blank" rel="noopener noreferrer" href="https://sites.google.com/view/mlmfgseminar/home">a webinar</a> on Machine Learning and Mean Field Games.</p>
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<a data-fancybox="gallery-gallery" data-options='{"idleTime" : "false"}' data-caption="Crowd motion with congestion effects: density of a crowd exiting a room with a mean field control model (paper <a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.3934/dcds.2015.35.3879">here</a>; see also <a target="_blank" rel="noopener noreferrer" href="http://dx.doi.org/10.1007/s00245-016-9342-8">here</a> and <a target="_blank" rel="noopener noreferrer" href="http://dx.doi.org/10.1007/s00245-016-9391-z">here</a> for another model)" href="/img/num-yaml1.png">
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<a data-fancybox="gallery-gallery" data-options='{"idleTime" : "false"}' data-caption="Machine learning for mean field games and mean field control: a min-LQG example (paper <a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/1908.01613">here</a>; see <a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/1907.05980">here</a> for the ergodic setting)" href="/img/num-rcml-dyn.png">
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<a data-fancybox="gallery-gallery" data-options='{"idleTime" : "false"}' data-caption="A penalization approach to non-smooth dynamical systems: phase diagram for a problem with obstacle (paper <a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1137/18M1173423">here</a>; on the same topic, see also <a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.3233/asy-171445">here</a>, <a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1051/proc/201448010">here</a> and <a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1051/proc/201965425">here</a>)" href="/img/penalizationNSS-figure10.png">
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<a href="/workingpaper/hu-2022-recent/" itemprop="url">Recent developments in machine learning methods for stochastic control and games</a>
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Ruimeng Hu, Mathieu Laurière</div>
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Sebastian Baudelet, Brieuc Frénais, Mathieu Laurière, Amal Machtalay, Yuchen Zhu</div>
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Gokce Dayanikli, Mathieu Laurière</div>
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<a href="/workingpaper/muller-2022-learning/" itemprop="url">Learning Correlated Equilibria in Mean-Field Games</a>
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Paul Muller, Romuald Elie, Mark Rowland, Mathieu Lauriere, Julien Perolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls</div>
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<a href="/workingpaper/lauriere-2022-learning/" itemprop="url">Learning mean field games: A survey</a>
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Mathieu Laurière, Sarah Perrin, Matthieu Geist, Olivier Pietquin</div>
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