4 papers in the archive (2023–2025), average empirical rigor 3.1/10, 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, Risk Management & Tail Risk, Volatility Modeling & Forecasting.
Frequent co-authors: Hoi Ying Wong (2), Ariel Neufeld (2), Kexin Chen (1), Junyan Ye (1), Mathieu Laurière (1), Daniel Bartl (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
We propose and analyze a framework for discrete-time robust mean-field control problems under common noise uncertainty. In this framework, the mean-field interaction describes the collective behavior of infinitely many cooperative agents’ state and action, while the common noise – a random disturba
We examine optimization problems in which an investor has the opportunity to trade in $d$ stocks with the goal of maximizing her worst-case cost of cumulative gains and losses. Here, worst-case refers to taking into account all possible drift and volatility processes for the stocks that fall within
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