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Nilanjan
Chatterjee
, PhD, MS

Bloomberg Distinguished Professor
Professor
Nilanjan Chatterjee

Departmental Affiliations

Primary
School of Medicine
Primary
Joint
Division
Genetic Epidemiology

Nilanjan Chatterjee, PhD, MS, models disease risk associated with genetic, lifestyle, biomarkers. and other factors, with the goal of improving disease prevention.

Contact Info

615 N. Wolfe Street, Room E3612
Baltimore
Maryland
21205
US        
301-402-0081

Research Interests

big data; cancer epidemiology and prevention; cancer genetics; epidemiologic study designs; genetic association studies; gene-environment interactions; risk prediction; statistical genetics

Experiences & Accomplishments
Education
PhD
University of Washington, Seattle
1999
MS
Indian Statistical Institute
1995
Overview

Scientific research: Dr. Chatterjee leads a broad research program in quantitative research that cuts across multiple areas of modern population-based biomedical science including statistical genetics/genomics, precision medicine and big data. The scientific goals of his studies include discovery of new biomarkers, understanding disease mechanisms, characterizing disease risk and developing risk-stratified approaches to disease prevention. He has extensively collaborated in recent genome-wide association studies that have led to identification new cancer susceptibility SNPs, provided characterization of heritability, genetic architecture and gene-environment interaction, and led to better understanding of potential for genetic risk stratification for cancer prevention. Prior to joining Hopkins, Dr. Chatterjee worked at the National Cancer Institute for 16 years and led the Biostatistics Branch of the Division of Cancer Epidemiology and Genetics during 2008-2015.

Methodologic studies: Dr. Chatterjee identifies important theoretical and methodological problems in statistics from his applied research. Motivated from genome-wide association studies,  for example, he has defined novel and robust methods for analyzing genetic associations, and gene-gene/gene-environment interactions. He has provided mathematical characterization of power of polygenic risk prediction models based on underlying genetic architecture of diseases. Outside the realm of statistical genetics, he has long-term interest in developing statistical methodologies for building predictive models combining data from disparate sources. Most recently, he has shown how models can be built based on individual level data from a given study utilizing summary-level information from external big-data sources.

Honors & Awards
  • Fellow of the American Statistical Association, 2008
  • Mortimer Spiegelman Award for 2010 from the American Public Health Association (outstanding contribution to public health by a leading statistician in the nation under age 40)
  • “Estimating effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 2010” featured as “leading edge article” in genomics by Cell.
  • George W. Snedecor Award for 2011 from the Committee of the Presidents of Statistical Societies (COPSS) (for instrumental contribution to theory of Biometry)
  • Presidents’ Award for 2011 from the Committee of the Presidents of Statistical Societies (COPSS) (for outstanding contribution to statistics by an individual under age 41)
  • Kwan Chao-Chih Distinguished Lecturer, Chinese Academy of Science, 2012
  • Elected member of the American Epidemiologic Society, 2012
  • Myrto Lefkopoulou Distinguished Lecturer, 2013, Harvard School of Public Health
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