Applications: Healthcare Management and Outcomes, Medical Imaging, Medical Informatics Methodologies: Longitudinal Data Analysis, Missing Data and Imputation, Statistics Relevant Projects:


Bin Nan

Professor, Biostatistics, School of Public Health


Statistics, College of Literature, Science, and the Arts

I am currently developing scalable methods for the estimation and inference of large covariance and precision matrices from temporally dependent data, focusing on the voxel-level brain connectivity. I am also involved in analyzing imaging data for Alzheimer’s disease, large healthcare data for the end stage renal disease, large epidemiological cohort data, and data from radiology studies.