Our research is guided by a single vision: to use quantitative intelligence to improve human health and economic well-being.

At HealthQuant Lab, we integrate methods from health economics, industrial organization, and machine learning (ML) to quantify how healthcare markets operate, why public health inequalities persist, and how to use data-driven methods to evaluate policies and promote fairness and efficiency.

Our research agenda is built on three interconnected pillars:

• Health Economics Analytics – applying econometric and modern data-science methods to evaluate how healthcare markets, innovations, and policies shape efficiency, equity, and population well-being.

• Evidence-Based Equity – uncovering and quantifying structural inequities through data-driven evaluation to advance fairness and evidence-based decision-making.

• Human-Centered Machine Learning – designing interpretable algorithms that prioritize social utility and public value over purely technical performance.

Through this interdisciplinary approach, we aim to transform how researchers, clinicians, and policymakers use data to build more equitable and efficient health systems.