4 papers in the archive (2022–2026), average empirical rigor 4/10, average math complexity 8.6/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Machine Learning for Market Forecasting, Options & Derivatives Pricing, Risk Management & Tail Risk.
Frequent co-authors: Daniel Chee (2), Libo Li (2), Jean-François Chassagneux (1), Junchao Chen (1), Stéphane Crépey (1), Azar Louzi (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 propose a new probabilistic scheme which combines deep learning techniques with high order schemes for backward stochastic differential equations belonging to the class of Runge-Kutta methods to solve high-dimensional semi-linear parabolic partial differential equations. Our approach notably exte
We propose a multilevel stochastic approximation (MLSA) scheme for the computation of the value-at-risk (VaR) and expected shortfall (ES) of a financial loss, which can only be computed via simulations conditionally on the realisation of future risk factors. Thus the problem of estimating its VaR an
This paper extends our previous work to continuous-time optimal stopping, focusing on American options in an exploratory setting. Our first contribution is an entropy-regularized penalization scheme, inspired by classical penalization techniques for reflected BSDEs. It yields a smooth approximation
Recent advances in continuous-time optimal stopping have been driven by entropy-regularized formulations of randomized stopping problems, with most existing approaches relying on partial differential equation methods. In this paper, we propose a fully probabilistic framework based on the Doob-Meyer-