About
Omolola (Lola) Ogunyemi is a computer scientist and biomedical informatics researcher who serves as the Director of CDU’s Center for Biomedical Informatics (CBI) and as Professor and Chair of the Department of Preventive and Social Medicine in the College of Medicine. She also holds an adjunct professorship in Radiological Sciences at the David Geffen School of Medicine at UCLA, where she collaborates with the Medical and Imaging Informatics group. She co-teaches MBS 520, Principles of Biomedical Informatics, a semester-long introduction to Biomedical Informatics, in CDU’s College of Science and Health.
Dr. Ogunyemi is an elected Fellow of the American College of Medical Informatics and has served on the National Library of Medicine’s Board of Regents both as a member and as board chair. She holds a B.A. in Computer Science from Barnard College, Columbia University, along with an M.S.E. and Ph.D. in Computer and Information Science from the University of Pennsylvania.
Research Interests
Her research at the CBI centers on novel biomedical informatics solutions designed to address challenges impacting medically underserved and under-resourced communities. Dr. Ogunyemi’s research interests include computerized medical decision support, reasoning under uncertainty, machine learning, telehealth, and 3D graphics and visualization.
She is currently working with LaShonda Spencer on a study that develops models to predict the failure of viral suppression and the likelihood of disengagement from care for individuals with HIV within the Los Angeles safety net, helping ensure that these individuals receive appropriate care.
She has served as principal investigator on an NLM-funded R01 study that used machine learning to identify safety net healthcare system patients with latent or undiagnosed diabetic retinopathy from electronic health records, which resulted in the DRRisk tool. She has also led an NLM-funded R01 study on computerized decision support for penetrating trauma and a National Cancer Institute-funded R03 study focused on individualized breast cancer risk prediction using Bayesian networks.