┏(＾0＾)┛ Hello, I’m Yuyi!
About Me
Originally from Shanghai, China, I am currently pursuing a PhD in Computational and Data Sciences at Washington University in St. Louis and am a doctoral scholar with the McDonnell International Scholars Academy. My research lies at the intersection of artificial intelligence, public health, computational social science, and health decision-making.
Artificial Intelligence · Public Health · Computational Social Science · Health Decision-Making
Research Focus
1. AI-Assisted Qualitative Research
My current work focuses on two main areas. First, I study how large language models and generative AI can support, replicate, and evaluate qualitative research workflows, including thematic analysis, grounded theory coding, and constant comparative analysis. I am interested in when LLMs can approximate human qualitative reasoning, where they diverge from expert interpretation, and how retrieval-augmented generation, multi-agent workflows, audit trails, and human evaluation can make AI-assisted qualitative analysis more transparent, traceable, and methodologically responsible.
2. LLM-Based Simulation and Decision-Making
Second, I develop LLM-based simulation and decision-making frameworks for public health, health economics, and social systems. This includes using AI agents to simulate economic preferences, survey responses, policy reasoning, behavioral dynamics, and collective decision-making. My work examines both the promise and the limitations of LLM agents as tools for modeling complex human and population-level processes.
3. Responsible AI for Health
My broader research interests include responsible AI for mental health services, digital health, vaccine safety surveillance, and AI-assisted healthcare decision-making. Across these areas, I am interested in developing reliable, interpretable, and culturally responsive AI systems that can support health communication, risk prediction, clinical and public health education, and real-world decision-making across diverse populations and resource settings.
Across my research, I emphasize rigorous evaluation, interpretability, traceability, and responsible AI implementation. Through interdisciplinary collaboration and methodological innovation, I aim to develop AI systems that complement traditional empirical methods while remaining technically robust, evidence-grounded, ethically accountable, empathetic, and useful for improving public health and social well-being.
Advisors
My advisors are Dr. Ruopeng An and Dr. Jiaxin Huang.