Jeffrey S. Morris, PhD
Professor of Biostatistics (appointment in process) / Director, Biostatistics Division
In addition to his professorial role, Dr. Morris serves as Director of the Division of Biostatistics. His research interests focus on developing quantitative methods to extract knowledge from biomedical big data, including work to relate complex biomedical object data—including functions, images and manifolds—to patient outcomes and characteristics using flexible, automated regression methods, and to integrate information across multiple types of multi-platform genomic, proteomic, imaging, and wearable device data to uncover biomedical insights contained in these complex data. He has done extensive applied work in cancer research, including constructing novel prognostic indices for hepatocellular carcinoma and helping develop and characterize molecular subtypes of colorectal cancer to discover new precision therapeutic strategies.
Dr. Morris is a Fellow of the Institute for Mathematical Statistics (IMS) and of the American Statistical Association (ASA), and has been recognized with various national honors, including the ASA’s Noether Young Scholar Award and Harvard University’s Myrto Leftkopoulou Distinguished Invited Lectureship. Dr. Morris was honored by M.D. Anderson with the 2018-19 Faculty Mentoring Award, given annually to one research faculty member in the institution. He has served as President of the East North American Region (ENAR) of the International Biometric Society and overall program chair for the Joint Statistical Meetings, the largest statistical meetings in the world. He also is currently the editor of Biology, Medicine and Genomics for the IMS journal The Annals of Applied Statistics and has served as an associate editor of several leading data science journals, including Biometrics and the Journal of the Royal Statistical Society, Series B.
Content Area Specialties
Cancer research, Colorectal cancer, Hepatocellular Carcinoma, Genomics, Computational Biology
Functional Data Analysis, Bayesian Modeling, Genomics, Integrative Modeling