I currently work as a Quantitative Researcher at Squarepoint Capital in New York.
I graduated from Stanford University and Ecole polytechnique, Paris. Prior to entering Ecole polytechnique, I was a Mathematics/Physics/CS classe préparatoire student at Lycee Sainte-Geneviève in Versailles.
I am broadly interested in the intersection of statistics/optimization/machine learning, with applications to the financial markets (but not only).
I really enjoy teaching. I was a teaching assistant in Mathematics at Lycee Sainte-Genevieve in the academic year 2016-2017. More recently, I have been a teaching assistant at Stanford for CS221 (AI: Principles and Techniques) in Fall 2018 and Spring 2019, as well as CS234 (Reinforcement Learning) in Winter 2020.
In my free time, I play a lot of music (acoustic and electric guitar). I also enjoy traveling and solving interesting puzzles.
Publications
- M. Emschwiller, B. Petit, J.-P. Bouchaud, “Optimal Multi-Asset Trading with Linear Costs: A Mean-Field Approach”, Quantitative Finance 21 (2), 185-195 (2020). Arxiv version: arxiv: 1905.04821.
- B. Petit, L. Amdahl-Culleton, Y. Liu, J. Smith, P.-L. Bacon, “All-Action Policy Gradient Methods: A Numerical Integration Approach”, NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop, arXiv: 1910.09093 (2019).
Contact
Feel free to reach out at:
benpetit [at] stanford [dot] edu