Bhramar Mukherjee

Bhramar Mukherjee is John D. Kalbfleisch Distinguished Professor, Professor of Epidemiology and Global Public Health at the University of Michigan School of Public Health; She also serves as the Associate Director for Quantitative Data Sciences at the University of Michigan Rogel Cancer Center. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of high dimensional exposure data. Bhramar and her team took an active role in modeling the SARS-CoV-2 virus trajectory in India during the pandemic, with the research being covered by major media outlets like Reuters, BBC, NPR, NYT, WSJ, Der Spiegel, Australian National Radio and the Times of India. She has co-authored more than 360 articles in statistics, biostatistics, medicine, and public health. 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: including the Gertrude Cox Award from the Washington Statistical Society in 2016, the L. Adrienne Cupples Award, from Boston University in 2020. In 2021 she was presented with the Distinguished Woman Scholar Award from Purdue University, the Janet L. Norwood award from the University of Alabama at Birmingham, the Sarah Goddard Power Award from the University of Michigan Academic Women’s Caucus, and most recently, in 2022 she was Elected as a Member of the US National Academy of Medicine and in 2023 she received the Karl E Peace Award for statistical contribution towards betterment of society awarded by The American Statistical Association.

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Linear Regression Modeling for Health Data (Coursera) Coursera
University of Michigan

Linear Regression Modeling for Health Data (Coursera)

Dive into the fundamentals of statistical modeling and linear regression analysis tailored specifically for health data. This course offers a structured approach to understanding and applying key concepts like t-tests and multiple regression models to analyze continuous outcomes in healthcare scenarios. Perfect for professionals looking to refine their skills in data interpretation and application within the medical field.

Jun 29th 2026
3 Weeks
Logistic Regression and Prediction for Health Data (Coursera) Coursera
University of Michigan

Logistic Regression and Prediction for Health Data (Coursera)

Dive into the world of health data analysis with our 'Logistic Regression and Prediction for Health Data' course. This course is designed to equip you with essential skills in analyzing binary outcomes, performing two-group comparisons, and applying statistical inference and prediction through logistic regression. Learn how to effectively use R to compare proportions, fit logistic regressions, make accurate predictions, and evaluate the quality of these predictions. Whether you're a healthcare professional or data enthusiast, this course will enhance your understanding and capabilities in health data analysis.

Jun 29th 2026
3 Weeks
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