BIO

Yajuan Si is a Research Associate Professor in the Survey Research Center and Associate Director of the Michigan Program in Survey and Data Science at the University of Michigan. She joined the University of Michigan in 2017 after serving for three years as a faculty member in biostatistics and population health at the University of Wisconsin–Madison. She earned her Ph.D. in Statistical Science from Duke University and completed postdoctoral training at Columbia University. Dr. Si’s research focuses on methodological development across the full data lifecycle, from study design to data analysis. Her work spans Bayesian statistics, the integration of design- and model-based approaches for survey inference, data integration, missing data imputation, confidentiality protection, and causal inference, with applications in the social and health sciences. She has developed a research agenda centered on advancing population inference through Bayesian modeling techniques and addressing selection and nonresponse bias in complex data settings. She is the recipient of the 2026 Gertrude Cox Award and an elected member of the International Statistical Institute.