Bin Nan

“The ultimate goal of my research is to improve human health by developing and applying statistical and machine learning methods.”

Statistics and Biostatistics

Chancellor’s 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

Chancellor’s 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 Chancellor’s 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.

Education

Ph.D., Biostatistics, University of Washington, 2001

M.S., Biostatistics, University of Washington, 1999

M.S., Statistics, Virginia Commonwealth University, 1997

M.S., Aerospace Engineering, Beijing University of Aeronautics& Astronautics, 1987

B.S., Aerospace Engineering, Beijing University of Aeronautics& Astronautics, 1984

Select Recent Publications

Hu B, Nan B (2023). Conditional distribution function estimation using neural networks for censored and uncensored data. Journal of Machine Learning Research (in press).

Xia L, Nan B, Li Y (2023). De-biased lasso for stratified Cox models with application to the national kidney transplant data. Annals of Applied Statistics (in press).

Wang Y, Nan B, Kalbfleisch JD (2023). Kernel estimation of bivariate time-varying coefficient model for longitudinal data with terminal event. Journal of the American Statistical Association (in press).

Xia L, Nan B, Li Y (2023). Statistical inference for Cox proportional hazards models with a diverging number of covariates. Scandinavian Journal of Statistics 50, 550-571.

Xia L, Nan B, Li Y (2023). De-biased lasso for generalized linear models with a diverging number of covariates. Biometrics 79, 344-357.

Li Y, Nan B, Zhu J (2021). A Structured Brain-wide and Genome-wide Association Study Using ADNI PET Images. Canadian Journal of Statistics 49, 182-202.

Shu H, Nan B (2019). Estimation of Large Covariance and Precision Matrices from Temporally Dependent Observations. Annals of Statistics 47, 1321-1350.

Kong S, Nan B, Kalbfleisch JD, Saran R, Hirth R (2018). Conditional modeling of longitudinal
data with terminal event. Journal of the American Statistical Association 113, 357-368.

Kong S, Nan B (2016). Semiparametric approach to regression with the covariate subject to a detection limit. Biometrika 103, 161-174.

Shu H, Nan B, Koeppe R (2015). Multiple testing for neuroimaging via hidden Markov random
field. Biometrics 71, 741-750.

Teaching

Stats 200A: Intermediate Probability and Statistical Theory I
Stats 200B: Intermediate Probability and Statistical Theory II
Stats 210B: Categorical Data Analysis
Stats 211: Generalized Linear Models
Stats 212: Analysis of Correlated and Longitudinal Data
Stats 220A: Advanced Probability and Statistical Theory
Stats 295: Advanced Topics in Survival Analysis
Stats 295: Advanced Topics in Semiparametric Models
Stats 295: Advanced Topics in High-Dimensional Inference