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Current Fellows in the Biostatistics Branch

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

See a list of BB Principal Investigators.

Neha Agarwala, Ph.D., Postdoctoral Fellow
Dr. Agarwala works on projects related to an estimation problem for survival data with special features and the combination of high-dimensional biomarkers to assess prevalence of infection with SARS- CoV-2.
Samuel (Sam) Anyaso-Samuel, Ph.D., Postdoctoral Fellow
Dr. Anyaso-Samuel develops statistical methods for the analysis of microbiome data and biological networks.
Jordan Aron, B.A., Predoctoral Fellow
Mr. Aron develops hidden Markov models for characterizing the heterogeneity of the wake cycle
Liana Hill, Prebaccalaureate Fellow
Ms. Hill is working on cancer risk analyses using imperfect cancer registry linkage data under the mentorship of Danping Liu, Ph.D., investigator.
Min Hua, Ph.D.
Dr. Hua works on developing novel statistical methods for mutational signatures extractions and their contributions to somatic mutations in cancer genomes.
Sang Kyu Lee, M.S., Predoctoral Fellow
Mr. 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.
Fangya Mao, Ph.D., Postdoctoral Fellow
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., Postdoctoral Fellow
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., Postdoctoral Fellow
Dr. Roberti investigates statistical methods as applied to studies of radiation, in particular related to the Chernobyl nuclear reactor accident.
Siddharth Roy, Ph.D., M.S.
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.
Joycelyn Williams, B.S.
Ms. Williams is exploring somatic LINE1 retrotransposon insertions in blood as potential cancer biomarkers.
Pei Zhang, M.S.
Ms. Zhang develops mixed modeling approaches to examine the relationship between high-dimensional biomarker/genomics data and subsequent cancer risk.