Jian Kang, PhD, MS

I am a Professor and Associate Chair for Research in Biostatistics at the University of Michigan, where I lead multidisciplinary research at the intersection of statistics, artificial intelligence, and biomedical science. My work focuses on developing advanced Bayesian and machine learning methods for large-scale neuroimaging, including fMRI, DTI, and EEG, as well as multi-omics data. A central theme of my research is designing interpretable, rigorous, and computationally scalable approaches that can bridge methodological innovation with impactful applications. These methods have been applied to pressing problems in mental health, neurodegenerative disease, and brain–computer interface systems, and I have directed NIH- and NSF-funded projects and data cores that advance collaborative science on a national scale.

In addition to research, I am deeply engaged in teaching and mentoring. I have been teaching graduate-level courses such as statistical computing and Bayesian inference, and I have supervised more than 20 PhD students and postdoctoral fellows who have gone on to careers in academia, industry, and government. I am also committed to curriculum innovation, working to integrate modern AI approaches into Bayesian inference and statistical modeling. Beyond the University, I am active in professional service, currently serving as Chair of the ASA Statistics in Imaging Section. Through research, teaching, and leadership, my work aims to advance statistical science and foster new insights into neuroscience, mental health, and precision medicine.