Michael Duprey

Michael Duprey is a research data scientist with over a decade of experience in mathematical modeling, agent-based modeling, Bayesian statistics, and machine learning.

Currently, Mr. Duprey supports NIH-funded epidemiological and public health-focused projects by developing mathematical and machine-learning models and deploying them on high-performance computing clusters. His current portfolio includes agent-based models of HIV/AIDS in Sub-Saharan Africa; agent-based models and stochastic models related to the opioid epidemic in the United States; and agent-based models of global cervical cancer. Mr. Duprey's work is instrumental in providing insights into public health challenges and potential interventions.

In his previous work at RTI, Mr. Duprey supported U.S. Department of Education’s National Center for Education Statistics projects by developing statistical models, conducting research into adaptive survey designs and nonresponse bias mitigation, and leading technical and methodological reporting. Mr. Duprey was also previously the lead programmer on RTI’s interactive data visualization platform for complex survey data (ATD Dashboard).

In addition to his work at RTI, Mr. Duprey holds several professional memberships. These include the Society for Industrial and Applied Mathematics (SIAM), SIAM Activity Groups on Algebraic Geometry and Data Science, Applied Algebraic Topology Research Network, and American Statistical Association. He is proficient in Python, R, Julia, PyTorch, and NetLogo.