3 papers in the archive (2024–2026), average empirical rigor 6/10, average math complexity 6.8/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Crypto Markets & DeFi, Market Microstructure, Risk Management & Tail Risk, Stochastic Control & Optimal Stopping.
Frequent co-authors: Zachary Feinstein (2), Dingyue Liu (1), Ciamac Moallemi (1), Xin Wan (1), Brian Zhu (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%).
Liquidity provision in automated market makers is typically analyzed at the pool level, implicitly assuming LP homogeneity. This aggregate view can hide how liquidity provision outcomes differ between LP strategies, particularly as concentrated liquidity AMM designs operating on high-performance blo
In this paper, we introduce an impact centrality measure to evaluate shock propagation on financial networks capturing a notion of contagion and systemic risk contributions, permitting comparisons of these risks over time. In addition, we provide a statistical validation method when the network is e
Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optima