5 papers in the archive (2024–2026), average empirical rigor 4/10, average math complexity 8.8/10. Quadrant mix: Holy Grail ×2, Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Reinforcement Learning for Trading, Fixed Income & Interest Rates, Portfolio Optimization.
Frequent co-authors: Jodi Dianetti (1), Giorgio Ferrari (1), Jin Ma (1), Ying Tan (1), Adel Javanmard (1), Jingwei Ji (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 study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corporate bonds, government bonds, loans, and other credit-related securities. A major challenge in pricing these securities stems from their infrequent trading and
We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomially growing rewards: settings that arise naturally in finance, economics, and operations research. To overcome the challenges of continuous and high-dim
This paper explores continuous-time and state-space optimal stopping problems from a reinforcement learning perspective. We begin by formulating the stopping problem using randomized stopping times, where the decision maker’s control is represented by the probability of stopping within a given time-
Motivated by recent empirical findings on the periodic phenomenon of aggregated market volumes in equity markets, we aim to understand the causes and consequences of periodic trading activities through a game-theoretic perspective, examining market interactions among different types of participants.
Generative AI can be framed as the problem of learning a model that maps simple reference measures into complex data distributions, and it has recently found a strong connection to the classical theory of the Schrödinger bridge problems (SBPs) due partly to their common nature of interpolating betwe