3 papers in the archive (2023–2026), average empirical rigor 6.2/10, average math complexity 7.3/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Machine Learning for Market Forecasting, Stochastic Control & Optimal Stopping, Options & Derivatives Pricing.
Frequent co-authors: Zhipeng Huang (1), Shuaiqiang Liu (1), Graziana Colonna (1), Lech A. Grzelak (1), Xianglin Wu (1), Chiheb Ben Hammouda (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%).
Stochastic differential equations (SDEs) driven by fractional Brownian motion (fBm) are increasingly used to model systems with rough dynamics and long-range dependence, such as those arising in quantitative finance and reliability engineering. However, these processes are non-Markovian and lack a s
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based nu
We propose the Compound BSDE method, a fully forward, deep-learning-based approach for solving a broad class of problems in financial mathematics, including optimal stopping. The method is based on a reformulation of option pricing problems in terms of a system of backward stochastic differential eq