4 papers in the archive (2023–2025), average empirical rigor 3.5/10, average math complexity 8.8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Reinforcement Learning for Trading, Insurance & Actuarial Risk, Statistical Arbitrage & Pairs Trading.
Frequent co-authors: Kyunghyun Park (2), Kexin Chen (1), Junyan Ye (1), Mei Choi Chiu (1), Ling Wang (1), Bingyan Han (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 and analyze a continuous-time robust reinforcement learning framework for optimal stopping under ambiguity. In this framework, an agent chooses a robust exploratory stopping time motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment
Empirical studies with publicly available life tables identify long-range dependence (LRD) in national mortality data. Although the longevity market is supposed to benchmark against the national force of mortality, insurers are more concerned about the forces of mortality associated with their own p
This paper studies robust time-inconsistent (TIC) linear-quadratic stochastic control problems, formulated by stochastic differential games. By a spike variation approach, we derive sufficient conditions for achieving the Nash equilibrium, which corresponds to a time-consistent (TC) robust policy, u
In a continuous-time economy, this paper formulates the Epstein-Zin preference for discounted dividends received by an investor as an Epstein-Zin singular control utility. We introduce a backward stochastic differential equation with an aggregator integrated with respect to a singular control, prove