3 papers in the archive (2023–2026), average empirical rigor 2.8/10, average math complexity 8.2/10. Quadrant mix: Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Reinforcement Learning for Trading.
Frequent co-authors: Gechun Liang (2), Moris S. Strub (2), Zhaojun Yang (1), Hoi Ying Wong (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 develop a framework for an investor who trades until she either reaches a financial goal or an exogenous deadline arrives. Analogous to utility functions over wealth, we measure satisfaction with the timing of reaching a goal by a discount function. For a continuous-time market where a stochastic
We introduce predictable relative forward performance processes (PRFPP) as a new framework for studying portfolio management within a competitive and incomplete market environment. Each agent trades a distinct stock following a binomial distribution with probabilities for a positive return depending
We propose an interactive robo-advising framework that learns personalized risk preferences from scores provided by clients. The resulting preference-learning problem is closely related to inverse reinforcement learning (IRL), as the robo-advisor infers the client’s latent reward specification from