My main research interests involve developing models and methodologies for complex biomedical data. I have developed approaches in information extraction from imperfect data due to measurement errors and incompleteness. My other methodology developments include model-based mixture modeling, non- and semiparametric modeling of longitudinal, dynamic and high dimensional data. I developed approaches that first gauge the effects of measurement errors on non-linear mixed effects models and provided statistical methods to analyze such data. Most methods I have developed are so called semi-parametric based. One strength of such approaches is that one does not need to make certain structure assumptions about part of the model. This modeling strategy enables data integration from measurements collected from sources that might not be completely homogeneous. My recently developed statistical methods focus on regularized approach and model building, selection and evaluation for high dimensional, dynamic or functional data.
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.