5 papers in the archive (2023–2026), average empirical rigor 6.2/10, average math complexity 8/10. Quadrant mix: Holy Grail ×4, Lab Rats ×1.
Works mostly in Portfolio Optimization, Risk Management & Tail Risk, Fixed Income & Interest Rates, Commodities & Energy Markets.
Frequent co-authors: Dohyun Ahn (1), Millend Roy (1), Vladimir Pyltsov (1), Yinbo Hu (1), Vijay Modi (1), Steven Campbell (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%).
Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overl
Valuing corporate bonds in systemic economies is challenging due to intricate webs of inter-institutional exposures. When a bank defaults, cascading losses propagate through the network, with payments determined by a system of fixed-point equations lacking closed-form solutions. Standard Monte Carlo
We study optimal portfolio choice for a household managing two goals at once. A random-deadline precautionary goal, such as a medical emergency, must be paid in full whenever it arrives and is affordable; a fixed-deadline accumulation goal, such as a target retirement lifestyle, may be declined at i
We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, two large language model (LLM) agents are assigned specialized tasks: one agent screens for firms with desirable fundamentals, while a sentiment analysis agent
We study Just-in-time (JIT) liquidity provision in blockchain-based decentralized exchanges. A JIT liquidity provider (LP) monitors pending swap orders in public mempools of blockchains to sandwich orders of their choice with liquidity, depositing right before and withdrawing right after the order.