Ismail El Moudden, DSc (PhD)
ASSISTANT PROFESSOR, ASSISTANT DIRECTOR
Healthcare Analytics and Delivery Science Institute
Dr. Ismail El Moudden is the Assistant Director of Data Science and Analytics and an Assistant Professor at Eastern Virginia Medical School (EVMS). He received his Ph.D. in statistics and data science after receiving his M.S in applied mathematics from the Mohammed V University in Rabat, Morocco. Prior to joining EVMS, he was at Mohammed V University, where he taught graduate level courses on applied mathematics and statistics. He also served as a visiting professor of data sciences at the International Academy Mohammed VI of Civil Aviation. As an Assistant Director of Data Science and Analytics at the EVMS-Sentara Healthcare Analytics and Delivery Science Institute (HADSI), Dr. El Moudden leads efforts by integrating advanced statistical methods and capabilities involving AI & machine learning to perform cutting-edge research in areas including healthcare economics, healthcare delivery, health services research, access and intervention, implementation science, epidemiology, rural health, health disparities, and prevention science. Since he joined HADSI in 2019, he worked as Biostatistician and Data Scientist consultant helping EVMS and Sentara researchers and other healthcare professionals including medical students, residents, fellows, and faculty in their research projects by providing ideas on refining research questions and hypotheses, epidemiology and study design, sample size calculation and power analysis, statistical analysis plan, in addition to statistical modeling and data analysis, as well as grant/paper/protocol preparation, writing, and editing.
Dr. El Moudden has an active and continuing research agenda, he has publications representing a well-balanced array of research in statistical learning. His primary research includes Healthcare Analytics, and as PI or co-Investigator on several research projects, he has laid the groundwork of developing effective feature selection and extraction using parsimonious factors and shrinkage methods. He works closely with collaborators in the fields of cancer, heart disease, neurology, surgery, and social determinants of health. His research focuses on statistical methods for analysis of electronic health records, high-dimensional data modeling, machine learning, prognostic modeling, correlated data, competing risks with applications to healthcare delivery and outcomes research.
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