Research Interest

• Understanding consumer decision-making under uncertainty, heterogeneity, behavioral biases and AI-mediated environments

• Modeling non-normative and context-dependent behavior, including present bias, delayed adoption, algorithmic reliance, and inconsistent choices

• Developing statistical, Bayesian, machine learning, and deep learning approaches for consumer behavior analysis, prediction, and risk assessment

• Designing and analyzing discrete choice experiments using efficient, adaptive, and augmented experimental frameworks

• Investigating how information architecture, interface design, and algorithmic systems influence consumer decisions, trust, and welfare

• Examining human-AI interaction, ethical AI design, explainability, and AI-driven UI/UX in digital service environments

• Analyzing multi-domain usage behavior and technology adoption across digital platforms, AI services, and emerging technologies

• Studying healthcare and aging-related decision-making, including technology acceptance and behavioral interventions for vulnerable populations

• Conducting interdisciplinary research connecting behavioral economics, marketing, data science, public policy, and applied statistics