Naisyin Wang, PhD, is Professor of Statistics, College of Literature, Science, and the Arts, at the University of Michigan, Ann Arbor.
Prof. Wang’s main research interests involve developing models and methodologies for complex biomedical data. She has developed approaches in information extraction from imperfect data due to measurement errors and incompleteness. Her other methodology developments include model-based mixture modeling, non- and semiparametric modeling of longitudinal, dynamic and high dimensional data. She 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 she has 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. Her recently developed statistical methods focus on regularized approach and model building, selection and evaluation for high dimensional, dynamic or functional data.
