Yi Guo, PhD, FAMIA

Dr. Yi Guo is Professor, Associate Chair for Data Science, and Division Chief (Preeminent Scholar) of Biomedical Informatics and Data Science in the Department of Health Outcomes and Biomedical Informatics at the University of Florida. He also serves as Chief Data Scientist and Director of the Cancer Informatics Shared Resource for the UF Health Cancer Institute, Associate Director of the Office of Data Science Research for the OneFlorida+ Clinical Research Network, Co-Director of the UF Clinical and Translational Science Institute (CTSI) Biomedical Informatics Program, and Co-Director of the Biostatistics and Informatics Core of the Claude D. Pepper Older Americans Independence Center.

Dr. Guo’s research focuses on developing and evaluating AI methods for clinical data, including electronic health records, administrative claims, and other observational data sources, with the goal of generating rigorous real-world evidence to support clinical and policy decision-making. His work integrates machine learning, causal inference, and study design to advance disease prevention, treatment evaluation, and population health management.

His research spans three interconnected methodological areas. First, he develops AI-driven phenotyping and risk prediction methods to identify clinical subpopulations and individuals at elevated risk using real-world clinical data. Second, he develops and applies causal inference and target trial emulation methods to estimate treatment effects and generate decision-relevant evidence from observational healthcare data. Third, he studies the evaluation of AI and causal models for real-world deployment, including model transportability across health systems, bias and confounding assessment, and the alignment of predictive and causal claims to support trustworthy clinical and policy decision-making.

Dr. Guo has authored more than 230 peer-reviewed publications (h-index 41) and serves as principal or multiple principal investigator on several NIH-funded R01 grants spanning real-world evidence generation, clinical and cancer informatics, and artificial intelligence in healthcare.

https://scholar.google.com/citations?hl=en&user=wnHzFS8AAAAJ