4 papers in the archive (2025–2026), average empirical rigor 6.2/10, average math complexity 8.2/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Market Microstructure, Portfolio Optimization, Reinforcement Learning for Trading, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Konark Jain (2), Daniel Cunha Oliveira (1), Grover Guzman (1), Ali Raza Jafree (1), Luis Ontaneda Mijares (1), Jonathan Kochems (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 optimal Market Making problem in a Limit Order Book (LOB) market simulated using a high-fidelity, mutually exciting Hawkes process. Departing from traditional Brownian-driven mid-price models, our setup captures key microstructural properties such as queue dynamics, inter-arrival cluste
Robust optimization provides a principled framework for decision-making under uncertainty, with broad applications in finance, engineering, and operations research. In portfolio optimization, uncertainty in expected returns and covariances demands methods that mitigate estimation error, parameter in
Overparameterized models have recently challenged conventional learning theory by exhibiting improved generalization beyond the interpolation limit, a phenomenon known as benign overfitting. This work introduces Adaptive Benign Overfitting (ABO), extending the recursive least-squares (RLS) framework
We investigate the mechanisms by which medium-frequency trading agents are adversely selected by opportunistic high-frequency traders. We use reinforcement learning (RL) within a Hawkes Limit Order Book (LOB) model in order to replicate the behaviours of high-frequency market makers. In contrast to