Applications: Brain Imaging, Healthcare Data Analysis, Large Cohort Studies, Radiology Methodologies: Large Matrix Estimation, Longitudinal Data Analysis, Missing Data, Multiple Testing, Regularized Regression, Survival Analysis Relevant Projects: NIH, NSF

Bin Nan

Professor, Biostatistics

Affiliation(s):

Statistics

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.