Profiles
Ismail El Moudden, DSc (PhD)
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Assistant Director, Assistant Professor, Obstetrics and Gynecology
Research and Infrastructure Service Enterprise (RISE)
Williams Hall
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. He has been accredited as a professional statistician (PStat) by the American Statistical Association. 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.
Faculty Appointments
Department of Obstetrics and Gynecology
Additional Certifications
PStat® (ASA)
Research Interests
Dr. EL Moudden research interests lie at the forefront of modeling, analysis, and classification of large-scale data based on dimensionality reduction methods using Machine Learning, Deep Learning and AI techniques. He has pursued a set of integrated activities for research, teaching, and service in the area of Biostatistics and Data Science. With his background in Statistics and Data Science, his goal is to integrate Machine Learning, Deep Learning and AI techniques with clinical research. The idea of precision medicine is of growing interest among scientific researchers and healthcare providers. During my time here, I have supported some clinical projects with about 5% of these studies assessing how these techniques can simplify the lives of patients, doctors, and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. Therefore, I would like to contribute to the expansion of this type of research; I believe this more novel type of research would be very beneficial to the research community and help strengthen the research culture as a whole.
Practice Locations
855 W. Brambleton Avenue
Norfolk
757.446.5908
Faculty Appointments
Department of Obstetrics and Gynecology
Additional Certifications
PStat® (ASA)
Research Interests
Dr. EL Moudden research interests lie at the forefront of modeling, analysis, and classification of large-scale data based on dimensionality reduction methods using Machine Learning, Deep Learning and AI techniques. He has pursued a set of integrated activities for research, teaching, and service in the area of Biostatistics and Data Science. With his background in Statistics and Data Science, his goal is to integrate Machine Learning, Deep Learning and AI techniques with clinical research. The idea of precision medicine is of growing interest among scientific researchers and healthcare providers. During my time here, I have supported some clinical projects with about 5% of these studies assessing how these techniques can simplify the lives of patients, doctors, and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. Therefore, I would like to contribute to the expansion of this type of research; I believe this more novel type of research would be very beneficial to the research community and help strengthen the research culture as a whole.
Practice Locations
855 W. Brambleton Avenue
Norfolk
757.446.5908