Bhramar Mukherjee, PhD
- John D. Kalbfleisch Collegiate Professor of Biostatistics
- Chair, Biostatistics
- Professor, Epidemiology
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; Faculty Affiliate, 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 has been engaged with the U-M Precision Health, an institution-wide presidential initiative for the last decade in various roles. 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 350 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. Bhramar is a member of the National Academy of Medicine. She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond including the Gertrude Cox award, the Adrienne Cupples award, the Janet Norwood award and the Sarah Goddard Power award. Bhramar and her team have been modeling the SARS-CoV-2 virus trajectory in India during the COVID-19 pandemic which has been covered by major media outlets like Reuters, BBC, NPR, NYT, WSJ, Der Spiegel, Australian National Radio and the Times of India.
- PhD, Statistics, Purdue University, 2001
- MS, Mathematical Statistics, Purdue University, 1999
- MStat, Applied Statistics and Data Analysis, Indian Statistical Institute, 1996
- BSc, 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. My interest over the years has shifted to integration of diverse data sources and scalable biobank data analysis. This includes integration of genetic, environmental and phenomic data. I am interested in principled analysis of electronic health records and other administrative healthcare data sources that were not designed for population-based research. One of my research themes is understanding selection bias across these diverse, heterogeneous data sources.
Mukherjee B, Chatterjee N. Exploiting gene-environment independence for analysis of case control
studies: An empirical-Bayes type shrinkage estimator to trade off between bias and
efficiency. Biometrics, 64(3):685-94, 2008, PMID 18162111.
Kastrinos F, Mukherjee B, Tayob N, Sparr J, Raymond VM, Wang F, Bandipalliam P, Stoffel EM, Gruber SB, Syngal S. The Risk of Pancreatic Cancer in Lynch Syndrome. Journal of the American Medical Association, 302(16):1790–95, 2009, PMCID: PMC4091624.
Mukherjee B, DeLancey JO, Raskin L, et al. Risk of Non-Melanoma Cancers in CDKN2A Mutation Carriers. The Journal of the National Cancer Institute, 104(12):953-56, 2012, PMCID: PMC3379723.
Boonstra PS, Mukherjee B, Taylor JMG. Bayesian shrinkage methods for partially observed high-dimensional data. The Annals of Applied Statistics, 7(4):2272–92, 2013, PMCID: PMC3891514.
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.
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, PMCID: PMC5954413.
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 Phenome-wide Study: Results from The Michigan Genomics Initiative. The American Journal of Human Genetics, 102:1048–1061, 2018, PMCID: PMC5992124.
Beesley LJ, Mukherjee B. Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification. Biometrics, 1-20,2020, PMID 33179768.
Gu T, Mack JA, Salvatore M, Sankar SP, Valley TS, Singh K, Nallamothu, BK Kheterpal, S, Lisabeth, L, Fritsche LG, Mukherjee B. Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System. JAMA Network Open, 3(10):e2025197, 2020, PMCID: PMC7578774.
Gu T, Taylor JMG, Mukherjee B. A meta-inference framework to integrate multiple external models into a current study. Biostatistics, 2021, kxab017, DOI: 10.1093/biostatistics/kxab01 7. PMID: 34269371.
Salvatore M, Purkayastha S, Ganapathi L, Bhattacharyya R, Kundu R, Zimmermann L, Ray D, Hazra A, Kleinsasser M, Solomon S, Subbaraman R, Mukherjee B. Lessons from SARS-CoV-2 in India: A data-driven framework for pandemic resilience. Sci Adv. 2022 Jun 17;8(24):eabp8621. doi: 10.1126/sciadv.abp8621. Epub 2022 Jun 17. PMID: 35714183; PMCID: PMC9205583.
M4208 SPH II
1415 Washington Heights
Ann Arbor, MI 48109-2029
Emily Klimas - Administrative Assistant
Deb Novak-Haas - Executive Assistant to the Chair
(734) 764-6544; email@example.com
Areas of Expertise: Biostatistics