Chatterjee was born in Kolkata, India, to parents Aditya Nath and Pranati Chatterjee. He attended Ballygunge Government High School and Saint Xavier's College. He received his bachelor's degree in statistics from the Indian Statistical Institute in 1993, and his master's in 1995. Chatterjee earned his PhD in Statistics from the University of Washington, Seattle in 1999. His PhD thesis, titled "Semiparametric Inference Based on Estimating Equations in Regression Models for Two-Phase Outcome-Dependent Sampling," was advised by Norman Breslow and Jon A. Wellner. Chatterjee joined the National Cancer Institute as a postdoctoral fellow in the biostatistics branch of the Division of Cancer Epidemiology and Genetics in 1999, and became a tenure-track investigator in 2001 and a senior investigator in 2004. He served as chief of the biostatistics branch from 2008 until 2015. He remains a Special Volunteer at the NCI. Chatterjee's wife is also a statistician. The two met at the Indian Statistical Institute in Kolkata.
Research
Chatterjee is known for his work inquantitative genetics, cancer research, big data, statisticalmethodology, genomics, gene-environment interaction, genetic association and genome-wide association studies. Chatterjee developed a model for predicting breast cancer risk that combines individual-level data on risk factors such as smoking and age with genetic variations associated with higher risk of breast cancer. He led a large collaboration of experts from the Johns HopkinsSchool of Medicine, the Bloomberg School of Public Health, and the Whiting School of Engineering that used big data and machine learning to develop tools for autism risk prediction and intervention. Chatterjee has participated in the Johns Hopkins Individualized Health Initiative, which aims to improve individual and population health outcomes through innovative collaborations among experts in biomedical and data science. Chatterjee's contributions to the initiative involved research on using genetic markers for risk prediction models as well as their applications to personalized medicine and public health interventions. Through his quantitative studies of gene-environment and gene-gene interactions, Chatterjee has also made fundamental contributions to theoretical and methodological approaches in epidemiology and biostatistics. Using statistics from genome-wide association studies to appraise the number of DNA variations that contribute to different physical traits and diseases, Chatterjee and colleagues developed a method for estimating the number of individual samples needed in order to identify genetic bases of traits, such as height or body mass index, or diseases, such as diabetes or bipolar disorder. This method can be used for designing genetic studies and understanding genetic risk prediction.
