From equations to impact: Advancing public health through biostatistics
Q&A with University of Michigan research Jian Kang on mental health, aging and cancer
By Bob Cunningham
Jian Kang’s name means “healthy” in Chinese, a choice his parents made with deep intention after his older brother was born with a serious medical condition.
Growing up in Beijing with this family experience of health challenges and recovery, Kang, PhD ’11, developed an early appreciation for the importance of health and well-being that would eventually shape his entire career in public health research.
While his name pointed toward health, Kang’s route to biostatistics unfolded through his love of mathematics and statistics. After earning his bachelor’s degree in Statistics from Beijing Normal University and a master’s degree in Mathematics from Tsinghua University, he came to the University of Michigan School of Public Health for his PhD in Biostatistics.
Today, 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.
What makes Kang’s research especially valuable is its direct impact on public health challenges. His statistical methods help doctors and researchers make sense of brain scans, improve early cancer detection, and better understand mental health conditions. He’s secured major grants from the National Institutes of Health (NIH) and National Science Foundation (NSF), and his work has influenced research around the world.
Beyond his own research, he is passionate about mentoring the next generation of data scientists, having guided more than 20 PhD students and postdoctoral fellows. His work demonstrates how mathematical expertise, combined with a commitment to health and healing, can create powerful tools for improving public health outcomes. Kang, who joined the Department of Biostatistics in 2015, was honored with the Excellence in Research Award at the Public Health Honors event in April.
How did witnessing your family’s health challenges shape your understanding of the role research plays in healthcare?
My interest in public health has deep personal roots. That early family experience with serious illness taught me several important lessons. Thankfully, my brother recovered fully and now thrives in his own career. Growing up with this experience gave me a lasting appreciation for the importance of health and inspired my commitment to advancing public health through research.
What specific areas of public health drive your statistical research, and what makes them compelling?
I’m especially interested in mental health, aging and cancer—three critical areas in public health that intersect with my research.
Mental health is increasingly recognized as a key component of overall well-being, yet it remains underdiagnosed and undertreated. Understanding its complex biological, social and environmental determinants is both challenging and vital.
Aging is another area of great importance as populations around the world grow older. Studying the aging process can help us identify predictors for early detection of cognitive decline, improve quality of life and inform healthcare policy.
Cancer continues to be one of the leading causes of death worldwide. I’m particularly drawn to the statistical challenges in early detection, personalized treatment, and integrating imaging, genomic and clinical data to improve health outcomes.
The best part of being an alumnus faculty member is that I’ve experienced the University of Michigan from both the student and faculty perspectives. Having been a PhD student here, I understand firsthand the academic rigor, the supportive environment and the many opportunities available to students.”
How did you discover your passion for applying statistics to biomedical problems?
I was drawn to biostatistics through a combination of rigorous mathematical training, solid computing and programming skills, and exposure to meaningful scientific problems, which I built during my undergraduate and master’s studies in China.
My interest in biostatistics deepened during my master’s thesis work under Professor Ying Yang, where we developed a novel regression model to study dynamic risk factors for interstitial cystitis. That project, which was later published in Statistics in Medicine, showed me how statistical modeling could be directly applied to address real biomedical questions.
What excites me most about biostatistics is its potential to drive progress in public health through rigorous, data-driven insights. It’s a discipline where methodological innovation and meaningful impact go hand in hand.
What drew you back to Michigan Public Health as a faculty member, and what’s special about returning to your alma mater?
I feel incredibly fortunate to have returned to my academic home as a faculty member. Beyond being an alumnus, several aspects of Michigan Public Health attracted me. First, the School is home to many world-leading experts in biostatistics and public health, offering an exceptional environment for collaboration and growth. Second, students at the School are among the most talented and hard-working I’ve encountered. It’s a joy to teach and conduct research with such self-motivated individuals. Third, the broader research ecosystem at the University of Michigan, including strengths in psychiatry, statistics, computer science, data science, and AI creates abundant opportunities for interdisciplinary, high-impact research.
On a personal note, my wife and I love living in Ann Arbor. It offers a great blend of small-town charm and vibrant city life. We especially enjoy the beautiful weather from May to October. After a decade in Ann Arbor, we’ve come to appreciate even the Michigan winters.
The best part of being an alumnus faculty member is that I’ve experienced the University of Michigan from both the student and faculty perspectives. Having been a PhD student here, I understand firsthand the academic rigor, the supportive environment and the many opportunities available to students. This perspective helps me relate to current students more effectively, whether in teaching, mentoring or guiding them through research.
It also informs how I think about developing research and educational programs within Michigan Public Health. I’m deeply invested in upholding the strengths that shaped my own training, while also contributing to innovation and growth in the department. It’s incredibly meaningful to give back to the institution that helped launch my career.
I see biostatistics as a discipline that both advances methodological theory and drives innovation in public health. That dual impact is what keeps me passionate about my work.”
What’s your primary research focus, and why is that blend of methodology and public health impact meaningful to you?
My main area of research lies at the intersection of statistical modeling, machine learning, and biomedical data science. I focus on developing principled Bayesian methods to analyze complex, high-dimensional data, particularly in areas like medical imaging, brain network analysis and brain-computer interfaces.
What drew me to this field is the opportunity to develop rigorous statistical tools that can be directly applied to important public health problems. I’m especially motivated by applications in mental health, infectious diseases and cancer—areas where data are often rich but challenging to analyze, and where better methods can lead to real clinical and scientific impact.
I see biostatistics as a discipline that both advances methodological theory and drives innovation in public health. That dual impact is what keeps me passionate about my work.
What’s something about public health you wish everyone knew?
I wish more people understood that behind every successful public health effort, whether it’s disease prevention, early diagnosis or improved health outcomes, there is a foundation of rigorous data analysis and careful study design.
Public health is driven by collaborative science and invisible infrastructure: surveillance systems, predictive models, and statistical research that help inform policies and clinical decisions. As a biostatistician, I’ve seen how thoughtful analysis of complex data, like brain imaging, electronic health records or large-scale cohort studies, can lead to life-changing insights, even if most people never hear about the methods behind them.
Public health may not always be visible, but its impact is everywhere.