Yi Huang joined UMBC in January of 2007, right after completing her Ph.D
in Biostatistics at Johns Hopkins University. Her research specializes in
statistical methods for the determination of average associational and
causal effects of treatment/exposure/intervention on adverse health
outcomes, with interested application areas in biomedical and aging
studies, public health and policy research. Her current methodological
interests include, but are not limited to, propensity scores and causal
modeling, latent variable models, and generalized regression methods. In
addtion to the work at UMBC, she collaborates with Bloomberg School of
Public Health of Johns Hopkins University, and Erickson School of Aging
Studies, and School of Medicine of University of Maryland.
Keywords: Biostatistics, causal inference, treatment/exposure effect,
aging study, biomedical research, public health and policy research.