Academic Staff

My research investigates how behavioral, psychological, and biological factors interact to shape health and treatment responses. A central question is how insights from these different levels can be translated into personalized, effective, and sustainable strategies for improving health.

Behavior, emotion, and dietary health

One major strand examines how people perceive and respond to stimuli, and how emotional, motivational, and contextual factors influence health behavior. In nutrition, I explore the intention–behavior gap: why knowledge and good intentions do not always lead to healthier food choices or sustained adherence to dietary recommendations. My research considers the roles of affective responses, self-regulation, reinforcement, perceived control, and individual differences.

In collaboration with industrial partners, I am developing scalable, AI-supported and gamified methods to assess cognitive, emotional, and motivational processes in laboratory and real-world settings. These approaches can measure behavioral markers such as attention, emotional bias, decision-making, flexibility, reward sensitivity, persistence, and achievement motivation. By combining task performance with self-report, physiological signals, and—where appropriate—automated behavioral analysis, we aim to improve the assessment of psychological states and support personalized feedback, counseling, and digital health interventions.

Traditional Chinese Medicine and systems biology

A second strand addresses formula–syndrome correspondence in Traditional Chinese Medicine (TCM). Rather than focusing only on isolated biomarkers or enrichment analyses, I investigate how syndrome patterns, therapeutic formulas, biological networks, and clinical outcomes relate to one another.

This work integrates information on herbal constituents, symptoms, pharmacological properties, and molecular mechanisms. Statistical learning, machine learning, causal inference, network analysis, and multi-omics approaches can help identify reproducible patterns associated with TCM syndromes and treatment responses. The broader aim is to determine whether TCM syndromes reflect distinct biological states, dynamic system-level configurations, or meaningful combinations of physiological and psychosocial features.

To support this research, I am developing a knowledge-management and visualization platform that represents relationships among symptoms, syndrome patterns, formulas, compounds, molecular targets, pathways, behavioral variables, and clinical outcomes. By organizing heterogeneous data into interpretable knowledge graphs and multilayer networks, the platform can help researchers compare patient groups, explore potential mechanisms, and generate testable hypotheses.

Shared methods and long-term direction

Across these strands, my group applies data science to complex, high-dimensional datasets. We combine AI and machine learning with statistical modeling, multi-omics integration, and network-based approaches to identify patterns across behavioral, and biological levels. We also use agentic AI to support research workflows, including literature discovery, data curation, knowledge extraction, code and analysis development, and hypothesis generation. These tools are intended to support—not replace—expert judgment, with an emphasis on transparency, reproducibility, validation, and human oversight.

Our long-term goal is to connect behavioral and psychological phenotypes with clinical and molecular mechanisms. This integrative approach may improve understanding of diet-related and mental health conditions, identify markers of risk and treatment response, and support patient stratification. Ultimately, we aim to contribute to precision nutrition and precision medicine by developing evidence-based methods for predicting individual needs and tailoring interventions.