I am a postdoctoral researcher at the Institute Louis Bachelier and Fondation du Risque in Paris.
My research develops mathematical and statistical foundations for learning and generating stochastic systems from dependent data, with particular emphasis on stochastic generative models, optimal transport, stochastic control, and PDE methods.
My current programme focuses on Schrödinger–Bass transport, statistical learning of stochastic bridge dynamics, path-space probability, and the stability and uncertainty quantification of generated path laws.
- Mathematical statistics for dependent data
- Stochastic generative models
- Optimal transport and stochastic control
- Schrödinger and Schrödinger–Bass bridges
- High-dimensional probability
- Reliable scientific machine learning
Direct nonparametric estimation of time-dependent Schrödinger-bridge drifts, with finite-sample, adaptive, asymptotic, and minimax guarantees.
A semimartingale optimal-transport framework that jointly controls drift and volatility.
Non-asymptotic analysis of least-squares estimation for dependent system-identification models.
- SBBTS: Schrödinger–Bass modelling for synthetic financial time series
- LightSBB-M: scalable Schrödinger–Bass generative modelling