3 papers in the archive (2025–2026), average empirical rigor 3.8/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Insurance & Actuarial Risk, Risk Management & Tail Risk, Portfolio Optimization, Reinforcement Learning for Trading.
Frequent co-authors: Maria Andraos (1), Mario Ghossoub (1), Benxuan Shi (1), Xia Han (1), Yuling Max Chen (1), David Saunders (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%).
Revisiting the continuous-time Mean-Variance (MV) Portfolio Optimization problem, we model the market dynamics with a jump-diffusion process and apply Reinforcement Learning (RL) techniques to facilitate informed exploration within the control space. We recognize the time-inconsistency of the MV pro
This paper studies optimal insurance design under asymmetric information in a Stackelberg framework, where a monopolistic insurer faces uncertainty about both the insured’s risk attitude, captured by a risk-aversion parameter, and the insured’s risk type, characterized by the loss distribution. In p
We study Stackelberg Equilibria (Bowley optima) in a monopolistic centralized sequential-move insurance market, with a profit-maximizing insurer who sets premia using a distortion premium principle, and a single policyholder who seeks to minimize a distortion risk measure. We show that equilibria ar