3 papers in the archive (2023–2026), average empirical rigor 2.8/10, average math complexity 8/10. Quadrant mix: Lab Rats ×3.
Works mostly in Stochastic Control & Optimal Stopping.
Frequent co-authors: Jose Blanchet (2), Yihang Sun (1), Yasa Syed (1), Mark S. Squillante (1), Mario Szegedy (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%).
The estimation of repeatedly nested expectations is a challenging task that arises in many real-world systems. However, existing methods generally suffer from high computational costs when the number of nestings becomes large. Fix any non-negative integer $D$ for the total number of nestings. Standa
This tutorial paper introduces quantum approaches to Monte Carlo computation with applications in computational finance. We outline the basics of quantum computing using Grover’s algorithm for unstructured search to build intuition. We then move slowly to amplitude estimation problems and applicatio
We study the estimation of repeatedly nested expectations (RNEs) with a constant horizon (number of nestings) using quantum computing. We propose a quantum algorithm that achieves $\varepsilon$-error with cost $\tilde O(\varepsilon^{-1})$, up to logarithmic factors. Standard lower bounds show this s