Dr. Chatterjee received his Bachelor’s and Master’s degree from the Indian Statistical Institute, Calcutta and a Ph.D. in statistics from the University of Washington, Seattle in 1999. He served as Chief of the Biostatistics Branch of the Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI) for almost eight years. Dr. Chatterjee now serves as a Bloomberg Distinguished Professor at the Johns Hopkins University with joint appointments at the Bloomberg School of Public Health (Biostatistics) and the School of Medicine (Oncology). He remains a Special Volunteer with DCEG.
Dr. Chatterjee's research focuses on a diverse set of quantitative issues that arise in design, analysis, interpretation and public health translation of modern molecular and genetic epidemiologic studies. He has made methodological contributions in cutting-edge topics such as analysis of genetic associations and gene-environment interactions, modeling etiologic heterogeneity by disease subtypes and development of models for risk prediction. He has played key roles in design and analysis of genome-wide association studies of various cancers with a particular emphasis on smoking related sites, such as lung and bladder. He is an active mentor for a number of Fellows and junior investigators and provides guidance for many ongoing projects in various areas such as risk-modeling, gene-environment interaction, design and analysis of studies that use next-generation sequencing. He is an elected Fellow of the American Statistical Association (2008), an elected member of the American Epidemiologic Society (2012) and is the recipient of numerous national and international awards, including the Mortimer Spiegelman Award (2010) for outstanding contribution to public health statistics, the Snedecor Award (2011) for significant contribution to theory of Biometry and the notable COPSS President’s award (2011) jointly sponsored by five major statistical societies to recognize the outstanding contribution by a young statistician.
- CBRM - An R package for testing Calibration of Binary Risk Model using different goodness-of-fit statistics
- CGEN - An R package for genetic analysis of case-control data.
- KinCohort - A MATLAB software package for likelihood-based analysis of kin-cohort data.
- MultAssoc - A MATLAB software package for test of association of a disease with a group of SNPs after accounting for their interaction with another group of SNPs or environmental exposures.
- SAS macro for haplotype analysis of case-control studies
- Integrated Power Calculation Tool
- ASSET - An R package for subset-based association analysis of heterogeneous traits and subtypes.