3 papers in the archive (2025), average empirical rigor 2.7/10, average math complexity 8.7/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Options & Derivatives Pricing, Risk Management & Tail Risk.
Frequent co-authors: Weixuan Xia (1), Jianfeng Zhang (1), Ying Tan (1), Renyuan Xu (1), Atiqah Almuzaini (1), Çağın Ararat (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 consider a Kyle-type model where insider trading takes place among a potentially large population of liquidity traders and is subject to legal penalties. Insiders exploit the liquidity provided by the trading masses to “camouflage” their actions and balance expected wealth with the necessary stea
Generative AI can be framed as the problem of learning a model that maps simple reference measures into complex data distributions, and it has recently found a strong connection to the classical theory of the Schrödinger bridge problems (SBPs) due partly to their common nature of interpolating betwe
We revisit the well-studied superhedging problem under proportional transaction costs in continuous time using the recently developed tools of set-valued stochastic analysis. By relying on a simple Black-Scholes-type market model for mid-prices and using continuous trading schemes, we define a dynam