Statistics and Biostatistics
Professor Bin Nan’s research interests are in various areas of statistics and biostatistics. He studies semiparametric inference, failure time and survival analysis, longitudinal data, missing data and two-phase sampling designs, high-dimensional data analysis, and machine learning methodology. He is also collaborating on projects in the areas of epidemiology, bioinformatics, and brain imaging. One study has focused on identifying functional connectivity in the brain. “Neurologists believe that connectivity between different regions in the brain may tell some story about how the brain works,” he says, explaining work to help neurologists estimate correlation or partial correlation coefficients between any two points in the brain. “The challenges are the temporal dependence among the sequence of images and the large number of points — voxels, for example — in the brain image leading to estimating large matrices.”
Biomedical Research Collaborations
Professor Nan’s research activities are constantly supported by National Science Foundation and National Institutes of Health grants, which are mostly motivated by problems arising from his collaborations in biomedical research. “I am currently focusing on the development of new methods and related theories in the areas of survival time prediction, high-dimensional statistical inference, analysis of high-dimensional brain imaging data, analysis of longitudinal data and disease onset data with terminal events, and regression with covariates subject to detection limits.”
Improving Human Health
“The ultimate goal of my research,” says Professor Nan, “is to improve human health by developing and applying statistical and machine learning methods to help evaluate risk factors and biomarkers, improve diagnosis, and eventually find cures for human diseases.” A particular area of interest is Alzheimer’s disease research. He is collaborating closely with investigators associated with the UCI Alzheimer’s Disease Research Center and the UCI Center for the Neurobiology of Learning and Memory to identify biomarkers that could lead to earlier diagnosis.