Current Fellows in the Biostatistics Branch

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Meet the fellows in the Biostatistics Branch (BB) and learn about their work.

Samuel (Sam) Anyaso-Samuel, Ph.D.
Dr. Anyaso-Samuel develops statistical methods for the analysis of microbiome data and biological networks.
Sang Kyu Lee, M.S.
Dr. Lee is working on quantile regression and survival analysis for high-dimensional data, especially about false discovery rate (FDR) control on variable selection and disparity analysis with cancer-related biomarkers.
Michael Kebede
Dr. Kebede applyies novel machine learning methods to characterize and forecast physical activity patterns and forecast in unique populations.
Fangya Mao, Ph.D.
Dr. Mao develops novel statistical methods for the analysis of multistate time-to-event data and the design of cost-effective biomarker studies, with an aim of advancing understanding of cancer etiology and complex cancer processes.
Fei Qin, Ph.D.
Dr. Qin develops statistical analysis methods for single cell sequencing data, including integration analysis with GWAS data, copy number estimation and spatially variable gene identification.
Sander Roberti, Ph.D.
Dr. Roberti investigates statistical methods as applied to studies of radiation, in particular related to the Chornobyl nuclear reactor accident.
Siddharth Roy, Ph.D.
Dr. Roy develops methods for identifying longitudinal biomarkers data for time-to-cancer outcomes in high-dimensional settings.
Jacob Williams, Ph.D.
Dr. Williams conducts research with the goal of creating a method to obtain polygenic risk scores from rare genomic variants and combine them with polygenic risk scores from common genomic variants to ultimately increase accuracy.
Pei Zhang, Ph.D.
Dr. Zhang develops mixed modeling approaches to examine the relationship between high-dimensional biomarker/genomics data and subsequent cancer risk.