Current Fellows in the Biostatistics Branch

Sumaja Bandreddi works on semi-continuous analyses of data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Dr. Bose develops novel statistical methods for GWAS and the integration of multi-omics data.

Dr. Chávez, postdoctoral fellow in BB, develops mathematical and computational methods for mutational signature analysis.

Dr. Joo develops statistical methods to analyze molecular marker studies in early-onset breast cancer and build microsimulation frameworks for oral cancer screening.

Dr. Kebede applyies novel machine learning methods to characterize and forecast physical activity patterns and forecast in unique populations.

Dr. Kim is developing novel statistical methods for analyzing mutational signatures that account for uncertainty in the downstream analysis.

Dr. Namhwa develops and applies novel statistical methods, particular emphasis on Machine Learning methods.

Ms. Lossman is working on multistate models for the natural history of cancers with precursor states.

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

Ms. Amara Saleem assists with the computational components of identifying genetic variants associated with lung cancer susceptibility.

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.

Dr. Veith studies tumor evolution and complex genomic features through whole-genome sequencing–based studies.

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