Biostatistics Results

How biostatistics can improve cancer care, pediatric heart health and health equity

A University of Michigan researcher explains how data, teamwork and statistical science can reveal disparities, guide care and improve health outcomes

University of Michigan biostatistician Mousumi Banerjee discusses her journey from Kolkata and mathematics to public health, cancer research, pediatric heart care and health equity. In this Q&A, she explains how biostatistics, machine learning and global partnerships can turn complex health data into evidence that improves care and lives for communities.

U-M School of Public Health professor Peter Song named 2025 AAAS fellow

Biostatistics professor Peter Song has been elected to the American Association for the Advancement of Science's 2025 class, recognized for his contributions to statistical methodology and public health data science.

AI tool can predict heart failure from genetic and health record data

A University of Michigan study demonstrates that combining genetic and clinical data for an individual patient can help predict heart failure, the world’s leading cause of hospitalization, a decade before it’s diagnosed.

On the Heights: December 2025

Michigan Public Health faculty and researchers advance digital health research, examine global school feeding programs, and share expertise on healthcare spending, noise pollution, and the power of public health storytelling.

Michigan Public Health welcomes six new faculty members

The University of Michigan School of Public Health added six new faculty members to start the Fall semester. They bring expertise in biostatistics, health equity, and reproductive health research.

From equations to impact: Advancing public health through biostatistics

Q&A with University of Michigan researcher Jian Kang on mental health, aging and cancer

Jian Kang is a professor and associate chair for Research in the Department of Biostatistics at Michigan Public Health, where he develops cutting-edge statistical methods for analyzing complex biomedical data. He’s particularly interested in brain-computer interfaces and neuroimaging, developing methods that help researchers better understand how the brain works and how it changes with age or disease.