3 papers in the archive (2025), average empirical rigor 7/10, average math complexity 6.8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Portfolio Optimization, Market Simulation & Agent-Based Models.
Frequent co-authors: Jinho Cha (3), Thi Le Hoa Vo (2), Jaeyoung Cho (2), Jaejin Lee (2), Sahng-Min Han (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%).
Effective supply chain management under high-variance demand requires models that jointly address demand uncertainty and digital contracting adoption. Existing research often simplifies demand variability or treats adoption as an exogenous decision, limiting relevance in e-commerce and humanitarian
This study develops an inverse portfolio optimization framework for recovering latent investor preferences including risk aversion, transaction cost sensitivity, and ESG orientation from observed portfolio allocations. Using controlled synthetic data, we assess the estimator’s statistical properties
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, w