We study how consumers make decisions under uncertainty, heterogeneity, and evolving technological environments, and how these decisions shape market and policy outcomes. Our research integrates behavioral economics, experimental design, AI-driven consumer analytics, and structural modeling to analyze, predict, and simulate human decision-making.

Methodologically, we employ discrete choice experiments, efficient and adaptive experimental designs, advanced choice models, Bayesian statistical approaches, and emerging machine learning and deep learning techniques to capture both behavioral mechanisms and predictive patterns.

Our research spans a broad range of application domains, including energy, finance, transportation, healthcare, and digital platforms. In particular, we increasingly focus on consumer behavior in AI-mediated environments, including ethical AI design, human-AI interaction, algorithmic decision-making, and AI-driven UI/UX research. Through interdisciplinary and data-driven approaches, our goal is to derive actionable managerial, technological, and policy implications for rapidly changing socio-technical systems.