Quentin Cormier

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Since October 2022, I am Chargé de Recherche at Inria Saclay, in the ASCII team.
In 2021-2022, I was a postdoctoral researcher at Princeton University, in the ORFE department.
I obtained my PhD at Inria Sophia-Antipolis, under the direction of Etienne Tanré and Romain Veltz, in the TOSCA team.

I am interested in the analysis of large assemblies of interacting particles/agents. I am studying mathematically models of spiking neurons, where the dynamics of each neuron is stochastic and can be described using point/jump processes. In the limit of large networks, the model is of mean-field type (McKean-Vlasov equation). I am analyzing the long time behavior of these objects, using both probabilistic and deterministic tools. I am also interested in the analysis of mean-field games and McKean-Vlasov control problems.

Address: CMAP, Ecole Polytechnique, Route de Saclay, 91128 Palaiseau. Office: 00 2033 9-3/4, aile 0.
ORCID iD iconhttps://orcid.org/0000-0002-2634-9134 Mail iconquentin.cormier@inria.fr

Publications

[Google Scholar, Arxiv, HAL]

Thesis Long time behavior of a mean-field model of interacting spiking neurons, defended the 15/01/2021 HAL.

7. Renewal theorems in a periodic environment
Preprint. Arxiv.

6. A mean-field model of Integrate-and-Fire neurons: non-linear stability of the stationary solutions
Mathematical Neuroscience and Applications (2024). Arxiv, HAL pdf

5. On the stability of the invariant probability measures of McKean-Vlasov equations
Accepted for publication at Annales de l'Institut Henri Poincaré. Arxiv.

4. Synchronization in a Kuramoto mean field game (with René Carmona and Mete Soner)
Communications in Partial Differential Equations (2023). Arxiv pdf

3. Hopf bifurcation in a Mean-Field model of spiking neurons (with Etienne Tanré and Romain Veltz)
Electronic Journal of Probability (2021). Arxiv, HAL pdf

2. Long time behavior of a mean-field model of interacting neurons (with Etienne Tanré and Romain Veltz)
Stochastic Processes and their Applications (2020) Arxiv, HAL pdf

1. Launch and Iterate: Reducing Prediction Churn (with Mahdi Milani Fard, Kevin Canini and Maya Gupta)
Advances in Neural Information Processing Systems 29 (NIPS 2016) pdf