PhD, 1994, Unversity of Washington

Dr. Lin's major statistical research interests lie in developing statististical methods for correlated and high dimensional data. Examples of correlated data include longitudinal data, clustered data, hierarchical data and spatial data. Examples of high dimensional data include genomic and proteinomics data. She is particularly interested in developing statistical and computational methods for "omics" data in population-based studies, such as genomic epidemiology and environmental sciences  Her statistical research is funded by the 2007 MERIT award from the National Cancer Institute on ``Statistical Methods for Correlated and High-Dimensional Biomedical Data" 

http://www.cancer.gov/researchandfunding/MERIT/Lin 

Dr. Lin's specific areas of statistical research include statistical learning methods for high-dimensional data, dimension reduction, variable selection, nonparametric  and semiparametric regression models, measurement error, mixed (frailty) models, estimating equations, missing data.

Dr. Lin's areas of applications include cancer, epidemiology, environmental health, genomics in population science, biomarkers and proteomics.