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.
Dr. Ismail El Moudden is 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 a Data Scientist and Biostatistician at the EVMS-Sentara Healthcare Analytics and Delivery Science Institute (HADSI), he ensures a rigorous, scientific evidence base for rapid-cycle health disparity research to increase the reach, external validity, and sustainment of effective cancer control and prevention interventions for the Cancer Health Disparities Outreach Program.
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. Dr. EL Moudden's 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.
Learning Decision by Hidden Markov Model.
Proceedings of the 24th International Business Information Management Association Conference - Creating Global Competitive Economies: 2020 Vision Strategic Planning and Smart Implementation.