3 papers in the archive (2023–2025), average empirical rigor 4.7/10, average math complexity 8.7/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Stochastic Control & Optimal Stopping, Volatility Modeling & Forecasting, Portfolio Optimization, Risk Management & Tail Risk.
Frequent co-authors: Jose Blanchet (2), Jiayi Cheng (2), Yang Liu (2), Tomasz R. Bielecki (1), Igor Cialenco (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 revisit Merton’s continuous-time portfolio selection through a data-driven, distributionally robust lens. Our aim is to tap the benefits of frequent trading over short horizons while acknowledging that drift is hard to pin down, whereas volatility can be screened using realized or implied measure
We consider a Bayesian diffusion control problem of expected terminal utility maximization. The controller imposes a prior distribution on the unknown drift of an underlying diffusion. The Bayesian optimal control, tracking the posterior distribution of the unknown drift, can be characterized explic
The aim of this work is to study risk measures generated by distortion functions in a dynamic discrete time setup, and to investigate the corresponding dynamic coherent acceptability indices (DCAIs) generated by families of such risk measures. First we show that conditional version of Choquet integr