Pierre Bras is a third-year PhD student at LPSM Sorbonne Université under the direction of Gilles Pagès. His research focuses on numerical methods for probability and statistics with applications to Machine Learning and Finance.

Interests

- Machine Learning
- Numerical Probability
- Stochastic Optimization
- Monte Carlo methods
- Mathematical Finance
- Stochastic control and Reinforcement Learning

Education

PhD in Applied Mathematics, 2020-2023

Sorbonne Université, LPSM

Master 2 Probability and Random Models, option Applied Probability, 2019

Sorbonne Université, LPSM

Diploma of Ecole Normale Supérieure in Mathematics and Applications, 2016-2020

Ecole Normale Supérieure de la rue d'Ulm

Langevin algorithms for Markovian Neural Networks and Deep Stochastic control.
In *arXiv e-prints*.

(2022).
Langevin algorithms for very deep Neural Networks with applications to image classification.
In *arXiv e-prints*.

(2022).
Total variation distance between two diffusions in small time with unbounded drift: application to the Euler-Maruyama scheme.
In *Electron. J. Probab.* 27, 1-19 (2022).

(2021).
Convergence of Langevin-Simulated Annealing algorithms with multiplicative noise .
In *arXiv e-prints*.

(2021).
- pierre.bras@sorbonne-universite.fr
- 4 Place Jussieu, Paris, 75005
- Couloir 16-26 Bureau 201