About Me
I am currently a fifth year Ph.D. Candidate of Biostatistics at the University of Michigan. I work in the Song Lab under the supervision of Professor Peter X.K. Song. I am especially interested in developing new methods for Big Data, for example, scalable and numerically stable methods for doing regression and machine learning. Besides methodological research, I have been working as a data manager and analyst in the Data Management and Modeling Core of the Children's Environmental Health and Disease Prevention Center at the University of Michigan since 2013.
Method Interests: Data integration and harmonization, high-dimensional statistical inference, regression parameter clustering, statistical computing, optimization.
Application Interests: Metabolomics, environmental health, epigenetics, bioinformatics, children’s health, statistical quality control.
Doctoral Dissertation Research:
- Develop and implement statistical methods for regression coefficient clustering in data integration. Applications include quantification of data heterogeneity, clustering of longitudinal patient trajectories, and detection of outlying studies.
- An R pacakge metafuse has been developed for the above purpose.
- Efficient and robust divide-and-conquer methods for generalized linear regression with variable selection and inference.