The Departments of Biostatistics & Medical Informatics and of Population Health Sciences are pleased to welcome new Assistant Professor Yajuan Si, PhD. Yajuan is joining us after completing her doctoral work in Statistical Science at Duke University and a post-doctoral fellowship at Columbia University under the direction of Professor Andrew Gelman. Her research interests are centered around Bayesian statistics and include latent variable models, complex survey inference methods, causal inference and post-stratification, and data confidentiality protection. Welcome Yajuan!
It has historically been a challenge to perform Bayesian inference in a design-based survey context. Yet, classical analysis is not robust especially for subgroup estimation. Model-based estimates are subject to bias under misspecified model assumptions. To address these problems, Yajuan develops a nonparametric Gaussian process regression of model-based survey inference and a unified framework under multilevel regression and post-stratification in realistic settings with survey weights.