4 papers in the archive (2024–2026), average empirical rigor 3.1/10, average math complexity 8.2/10. Quadrant mix: Lab Rats ×4.
Works mostly in Stochastic Control & Optimal Stopping, Reinforcement Learning for Trading, Insurance & Actuarial Risk, Volatility Modeling & Forecasting.
Frequent co-authors: Tim J. Boonen (1), Kenneth Tsz Hin Ng (1), Tak Wa Ng (1), Silvana M. Pesenti (1), Huy Chau (1), Duy Nguyen (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%).
In a reinforcement learning (RL) framework, we study the exploratory version of the continuous time expected utility (EU) maximization problem with a portfolio constraint that includes widely-used financial regulations such as short-selling constraints and borrowing prohibition. The optimal feedback
We consider the problem of active portfolio management, where an investor seeks the portfolio with maximal expected utility of the difference between the terminal wealth of their strategy and a proportion of the benchmark’s, subject to a budget and a deviation constraint from the benchmark portfolio
We study the problem of optimal portfolio selection under stochastic volatility within a continuous time reinforcement learning framework with portfolio constraints. Exploration is modeled through entropy-regularized relaxed controls, where the investor selects probability distributions over admissi
We propose a peer-to-peer (P2P) insurance scheme comprising a risk-sharing pool and a reinsurer. A plan manager determines how risks are allocated among members and ceded to the reinsurer, while the reinsurer sets the reinsurance loading. Our work focuses on the strategic interaction between the pla