4 papers in the archive (2022–2026), average empirical rigor 6.6/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, Market Simulation & Agent-Based Models, Stochastic Control & Optimal Stopping.
Frequent co-authors: Mohamed Hamdouche (1), Pierre Henry-Labordere (1), Alexandre Alouadi (1), Grégoire Loeper (1), Célian Marsala (1), Othmane Mazhar (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
We study generative modeling for time series using entropic optimal transport and the Schrödinger bridge (SB) framework, with a focus on applications in finance and energy modeling. Extending the diffusion-based approach of Hamdouche, Henry-Labordère, Pham, 2023, we introduce a jump-diffusion Schröd
We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existing approaches typically fail to jointly model drift and stochastic volatility, as diffusion-based methods fix the volati
We propose a novel generative model for time series based on Schr{ö}dinger bridge (SB) approach. This consists in the entropic interpolation via optimal transport between a reference probability measure on path space and a target measure consistent with the joint data distribution of the time series
This paper is devoted to the numerical resolution of McKean-Vlasov control problems via the class of mean-field neural networks introduced in our companion paper [25] in order to learn the solution on the Wasserstein space. We propose several algorithms either based on dynamic programming with contr