Overview
I am interested in developing Bayesian statistical methods for comparative effectiveness research, network meta-analysis, causal inference, measurement error, and generalizability. A flexible Bayesian modeling framework enables us to easily integrate different data sources and borrow information adaptively across them. The ultimate goal of my research is to provide comprehensive evidence from multiple data sources for answering clinical and scientific questions in public health and medicine.
Current Appointments & Affiliations
Associate Professor of Biostatistics & Bioinformatics
·
2023 - Present
Biostatistics & Bioinformatics, Division of Biostatistics,
Biostatistics & Bioinformatics
Member in the Duke Clinical Research Institute
·
2018 - Present
Duke Clinical Research Institute,
Institutes and Centers
Education, Training & Certifications
University of Minnesota, Twin Cities ·
2013
Ph.D.
Harvard University ·
2010
M.S.