Bhramar Mukherjee, PhD
- John D. Kalbfleisch Collegiate Professor of Biostatistics
- Chair of Biostatistics
- Professor of Epidemiology
- Professor of Global Public Health
School of Public Health, University of Michigan
- Research Professor, Michigan Institute of Data Science
- Associate Director for Quantitative Data Sciences, University of Michigan Rogel Cancer Center
Bhramar Mukherjee is John D. Kalbfleisch Collegiate Professor and Chair, Department of Biostatistics; Professor, Department of Epidemiology, Professor, Global Public Health, University of Michigan (UM) School of Public Health; Research Professor and Core Faculty Member, Michigan Institute of Data Science (MIDAS), University of Michigan. She also serves as the Associate Director for Quantitative Data Sciences, University of Michigan Rogel Cancer Center. She is the cohort development core co-director in the University of Michigan’s institution-wide Precision Health Initiative. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of multiple pollutants. Collaborative areas are mainly in cancer, cardiovascular diseases, reproductive health, exposure science and environmental epidemiology. She has co-authored more than 200 publications in statistics, biostatistics, medicine and public health and is serving as PI on NSF and NIH funded methodology grants. She is the founding director of the University of Michigan’s summer institute on Big Data. Bhramar is a fellow of the American Statistical Association and the American Association for the Advancement of Science. She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond.
- PhD, Statistics, Purdue University, 2001
- M.S., Mathematical Statistics, Purdue University, 1999
- M.Stat., Applied Statistics and Data Analysis, Indian Statistical Institute, 1996
- B.Sc., Statistics, Presidency College, 1994
- The central theme in my research program has been to develop novel inferential methods for epidemiological data using Bayesian, frequentist and hybrid methods. Epidemiology is a science which progresses by accumulation of evidence, and the Bayesian paradigm offers many natural solutions to complex problems encountered in modern epidemiology. Currently, I am working on case-control studies of gene-environment interaction, two-phase studies and longitudinal studies. I am interested in foundational issues related to outcome dependent or exposure enriched sampling. Recently I have been interested in shrinkage estimation and prediction using diverse heterogeneous data sources.
- Sun Z, Tao Y, Li S, Ferguson KK, Meeker JD, Park SK, Batterman SA, Mukherjee B. Statistical strategies for constructing health risk models with multiple pollutants
and their interactions: possible choices and comparison. Environmental Health, 12(1):85, 2013, PMCID: PMC3857674.
- *He Z, Zhang M, Lee S, Smith JA, Guo X, Palmas W, Kardia SLR, Diez-Roux AV, Mukherjee B. Multi-marker tests for joint association in longitudinal studies using the genetic
random field model. Biometrics, 71(3):606-15, 2015, PMCID: PMC4601568.
- Ferguson KK, Chen Y-H, VanderWeele TJ, McElrath TF, Meeker JD, Mukherjee B. Mediation of the relationship between maternal phthalate exposure and preterm birth
by oxidative stress with repeated measurements across pregnancy. Environmental Health Perspective, 125(3): 488-494, 2017, PMCID: PMC5332184.
- *He Z, Zhang M, Lee S, Smith JA, Kardia SLR, Diez Roux AVD, Mukherjee B, Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies. The Journal of the American Statistical Association, Application and Case Studies, 112(519):966-978, 2017.
- *Sun Z, Mukherjee B, Estes JP, Vokonas P and Park SK, Exposure enriched outcome-dependent sampling designs
for longitudinal studies of gene-environment interaction. Statistics in Medicine, 36(18):2947-2960, 2017, PMCID: PMC5523112.
- Estes JP, Rice JD, Li S, Stringham HM, Boehnke M and Mukherjee B, Meta-Analysis of Gene-Environment Interaction Exploiting Gene-Environment Independence
Across Multiple Studies. Statistics in Medicine. 36(24):3895-3909, 2017, PMCID: PMC5624850.
- *Liu G, Lee S, Lee A, Others, Pearce CL, Mukherjee B. Robust tests for additive gene-environment interaction in case-control studies.
The American Journal of Epidemiology, 187(2):366-377, 2018, PMCID: PMC5860584.
- *Cheng W, Taylor JMG, Vokonas P, Park SK, Mukherjee B. Improving estimation and prediction in linear regression incorporating external
information from an established reduced model. Statistics in Medicine, 37(9):1515-1530, 2018, PMCID: PMC5889759.
- Wagner AL, Xia L, Pandey P, Datta S, Chattopadhyay S, Mazumder T, Sujay Santra S,
Nandi U, Pal J, Joshi S, Mukherjee B. Risk factors during pregnancy and early childhood in rural West Bengal, India: A
feasibility study implemented via trained community health workers using mobile data
collection devices. Maternal and Child Health, 2018, doi: 10.1007/s10995-018-2509-y. [Epub ahead of print], PMID: 29500782.
- Fritsche L, Gruber SB, Wu Z, Schmidt EM, Zawistowski M, Moser SE, Blanc V, Brummet
C, Kheterpal S, Abecasis GA, Mukherjee B. Association of Polygenic Risk Scores for Multiple Cancers in a Phenomewide Study:
Results from The Michigan Genomics Initiative, The American Journal of Human Genetics, 102:1048-1061, 2018, PMCID: PMC5992124.
- *Chen Y-H, Mukherjee B, Adar S, Berrocal V, Coull BA, Robust Distributed Lag Models using Data Adaptive
Shrinkage. Biostatistics, 2017, [Epub ahead of print], PMID: 29040386.
- *Chen Y-H, Mukherjee B and Berrocal V, Distributed Lag Interaction Models with Two Pollutants. JRSS, Series C., Applied Statistics, 68(1):79-97, 2019, PMCID: PMC6328049.
*The first author was a graduate student of Dr. Mukherjee at the time of this research.