معرفی
Dr. Fan Li is an Associate Professor in the Department of Biostatistics at Yale School of Public Health with a secondary appointment in the Department of Internal Medicine (Section of Cardiovascular Medicine) at Yale School of Medicine. He is affiliated with the Center for Methods in Implementation and Prevention Science, Yale Center for Analytical Sciences, and the Clinical and Translational Research Accelerator.
Dr. Li's research focuses on:
- Advancing statistical methodology in causal inference
- Improving clinical trial design, particularly cluster randomized trials and stepped wedge designs
- Developing mediation analysis techniques
- Integrating machine learning with causal inference methods
- Creating robust statistical software for practical implementation
His work integrates semiparametric and machine learning techniques to address complex challenges in pragmatic trials, clinical research, and public health. He has developed methods to enhance trial efficiency, improve causal effect estimation, and uncover causal mechanisms through advanced mediation analysis.
Dr. Li has published over 150 peer-reviewed articles and developed statistical software packages including PSweight, CoxBCV, mediateP, cvcrand, and geeCRT. His research spans cardiovascular medicine, nephrology, aging, dementia, emergency medicine, and critical care.
His scientific awards include:
- Early Career Investigator Research Award (2022)
- Student Paper Award (2019)
- Chancellor's Award for Research Excellence (2018)
- Distinguished Student Paper Award (2018)
- Student Travel Award (2015)
Dr. Li serves as Co-Editor-in-Chief for Epidemiologic Methods and Associate Editor for Statistics in Medicine, Annals of Applied Statistics, and Clinical Trials. He mentors students and postdoctoral researchers at the intersection of statistical methodology and real-world application.
His lab, Whisk Cup, focuses on developing principled methodologies that integrate modern causal machine learning to improve robustness, efficiency, and interpretability in clinical trials and observational studies.
