6 papers in the archive (2025), average empirical rigor 6.4/10, average math complexity 6.2/10. Quadrant mix: Holy Grail ×4, Lab Rats ×1, Street Traders ×1.
Works mostly in Volatility Modeling & Forecasting, Portfolio Optimization, Market Simulation & Agent-Based Models, Crypto Markets & DeFi.
Frequent co-authors: Long Pham (3), Junyeol Ryu (2), Hyeyoung Hwang (2), Thi Le Hoa Vo (2), Jaeyoung Cho (2), Jaejin Lee (2).
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
This study introduces an inverse behavioral optimization framework that integrates QALY-based health outcomes, ROI-driven incentives, and adaptive behavioral learning to quantify how policy design shapes national healthcare performance. Building on the FOSSIL (Flexible Optimization via Sample-Sensit
Background: Chronic diseases impose a sustained burden on healthcare systems through progressive deterioration and long-term costs. Although adherence-enhancing interventions are widely promoted, their return on investment (ROI) remains uncertain, particularly under heterogeneous patient behavior an
This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution. While existing research emphasizes the technical feasibility of smart contracts, the operational and economic implicatio