Research
AI designed to complement rigorous human inquiry.
I develop and evaluate agentic AI systems for qualitative inquiry, public health simulation, and human-centered decision-making across social and healthcare contexts.
01AI-Assisted Qualitative Research
I examine how large language models can support thematic analysis, grounded theory coding, and constant comparative analysis—where they approximate expert interpretation, where they diverge, and how retrieval, multi-agent workflows, audit trails, and human evaluation can make their use transparent and methodologically sound.
02Simulation & Decision-Making
I develop LLM-based frameworks that simulate economic preferences, survey responses, policy reasoning, behavioral dynamics, information diffusion, and collective decision-making. This work evaluates both the promise and limits of AI agents as models of complex human and population-level systems.
03Responsible AI for Health
I develop reliable, interpretable, and culturally sensitive AI for mental health, digital health, vaccine safety surveillance, health communication, and public health education—grounded in rigorous evaluation, traceability, ethical accountability, and practical utility.
MethodEvidence-groundedAI systems should complement established empirical and qualitative methods rather than obscure them.
DesignHuman-centeredTools should remain interpretable, culturally sensitive, and accountable to the people affected by them.
ImpactDecision-relevantResearch should support better health communication, policy reasoning, and real-world decisions.
Collaborate
I welcome interdisciplinary collaborations spanning AI, public health, and social work.
Beyond research
I enjoy Chinese cooking and dessert-making, making music on the piano, violin, and harmonica, and playing badminton.