4 papers in the archive (2023–2026), average empirical rigor 3.8/10, average math complexity 8.4/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping, Market Microstructure, Reinforcement Learning for Trading, Portfolio Optimization.
Frequent co-authors: Philippe Bergault (2), Robert Boyce (1), Martin Herdegen (1), Lane P. Hughston (1), Yadh Hafsi (1), Sébastien Bieber (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 problem of optimal liquidity withdrawal for a representative liquidity provider (LP) in an automated market maker (AMM). LPs earn fees from trading activity but are exposed to impermanent loss (IL) due to price fluctuations. While existing work has focused on static provision and exogen
We develop a mixed control framework that combines absolutely continuous controls with impulse interventions subject to stochastic execution delays. The model extends current impulse control formulations by allowing (i) the controller to choose the mean of the stochastic delay of their impulses, and
We study liquidity provision in the presence of exogenous competition. We consider a reference market maker' who monitors her inventory and the aggregated inventory of the competing market makers. We assume that the competing market makers use a rule of thumb’ to determine their posted depths, dep
We consider a rational agent who at time $0$ enters into a financial contract for which the payout is determined by a quantum measurement at some time $T>0$. The state of the quantum system is given in the Heisenberg representation by a known density matrix $\hat p$. How much will the agent be willi