Jing Su Email and Phone Number

Dr. Su is currently an Associate Professor at the Department of Biostatistics and Health Data Science, Indiana University School of Medicine. He also serves as the Director of Data Management Services at Biostatistics and Health Data Science, and the Associate Director of Real-work Data at the Biostatistics and Data Management Core of the IU Health Simon Comprehensive Cancer Center. He is interested in biomedical informatics that incorporates electronic health records, graph-based machine learning, big data integration and mining, and computational models 1) to identify clinical phenotypic traits of patient subgroups that demonstrate different risk factors, disease development patterns, and treatment responses; 2) to understand the underlying mechanisms of such subgroups from the perspective of the dynamics of complex biosystems; and 3) to apply actionable knowledge in precision medicine and clinical decision-making. He currently focuses on three closely related directions: biomedical informatics for precision medicines, graph theory in machine learning and biomedical big data mining, and immune systems biology. Dr. Su is interested in three closely correlated chronic complex diseases: kidney diseases, cancers, and Alzheimer's disease. Dr. Su obtained his Ph.D. degree in Biomedical Engineering (2008) from Georgia Institute of Technology and Emory University at Atlanta, Georgia (Advisor: Dr. J. Carson Meredith) on biomaterial informatics and high-dimensional combinatorial libraries. He worked with Dr. Michael Henson (2009) in the University of Massachusetts Amherst to investigate network motifs underlying the circadian control of cell cycle, and with Dr. Xiaobo Zhou (2010) in The Methodist Hospital Research Institute and Weill Cornell Medical College of Cornell University at Houston, Texas to apply systems biology for clinical challenges. Dr. Su joined Wake Health in January, 2013 to advance graph-based machine learning in clinical and biomedical big data. Specialties: biomedical and clinical informatics, graph artificial intelligence, systems biology, biomaterials, and cellular biology.