University of Missouri

Sounak Chakraborty is a Professor in the Department of Statistics and Data Science at the University of Missouri and a Core Faculty member of the MU Institute for Data Science and Informatics. His research lies at the intersection of Bayesian statistics, machine learning, artificial intelligence, and data science, with a particular emphasis on developing statistically rigorous methods for learning from complex, high-dimensional, heterogeneous, and distributed data. His methodological contributions span Bayesian machine learning, federated learning, generative AI, variable selection, kernel methods, survival analysis, spatio-temporal modeling, multi-omics data integration, and uncertainty quantification.

Prof. Chakraborty is passionate about translating statistical innovation into real-world impact. His methods have been applied to a wide range of scientific and societal challenges, including cancer genomics, microbiome research, precision medicine, electronic health records, environmental epidemiology, climate change, public health, infectious diseases, agriculture, and disease ecology. His work has been supported by the National Science Foundation (NSF), National Institutes of Health (NIH), NASA, USDA, and other agencies, and has led to the development of several widely accessible software tools and R packages for the research community.

An elected member of the International Statistical Institute (ISI) and Fellow of the Royal Statistical Society (RSS), Prof. Chakraborty is actively engaged in advancing the statistical profession through editorial leadership, interdisciplinary collaboration, mentoring, and outreach. His current research focuses on trustworthy AI, Bayesian machine learning, privacy-preserving statistical inference, federated learning, multi-modal data integration, and scalable methods for scientific discovery in the era of big data and artificial intelligence.