Current Fellows in the Biostatistics Branch

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.

Dr. Anyaso-Samuel develops statistical methods for the analysis of microbiome data and biological networks.

Mr. Aron develops hidden Markov models for characterizing the heterogeneity of the wake cycle

Dr. Étiévant works on developing statistical methods for causal inference in longitudinal datasets and methods for high-dimensional mediation analysis, and on inference for HPV vaccine trials.

Ms. Hill is working on cancer risk analyses using imperfect cancer registry linkage data under the mentorship of Danping Liu, Ph.D., investigator.

Dr. Hua works on developing novel statistical methods for mutational signatures extractions and their contributions to somatic mutations in cancer genomes.

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.

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.

Dr. Miranda-Filho is conducting descriptive epidemiology research by applying statistical and mathematical models to elucidate emerging trends in cancer incidence, mortality, and survivorship among world populations.

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.

Dr. Roberti investigates statistical methods as applied to studies of radiation, in particular related to the Chernobyl nuclear reactor accident.

Dr. Roy develops methods for identifying longitudinal biomarkers data for time-to-cancer outcomes in high-dimensional settings.

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.

Ms. Williams is exploring somatic LINE1 retrotransposon insertions in blood as potential cancer biomarkers.

Ms. Zhang develops mixed modeling approaches to examine the relationship between high-dimensional biomarker/genomics data and subsequent cancer risk.