Paul G Dupuis

Paul G Dupuis is the IBM Professor of Applied Mathematics at Brown University. His research focuses on applications of probability theory, stochastic processes, control theory, and numerical methods. He holds affiliations with the American Mathematical Society, Society for Industrial and Applied Mathematics (SIAM), and the Institute for Mathematical Statistics (IMS). His work emphasizes large deviation theory, Markov chain approximations, Monte Carlo simulation, and partial differential equations.

Education: Ph.D. in Applied Mathematics from Brown University (1985), M.S. from Northwestern University (1982), and B.S. from Brown University (1981).

Research Interests: Control of deterministic and stochastic processes, differential games, numerical methods, operations research, and stochastic processes. His contributions include foundational work on large deviation theory, risk-sensitive control, and queueing networks.

Awards: Elected SIAM Fellow (2010), Fellow of the Institute for Mathematical Statistics (2011), IBM Professor of Applied Mathematics (2012), and AMS Fellow (2014). Previously held an NSF Postdoctoral Fellowship (1985-1988).

Grants: Current funding from the Army Research Office and National Science Foundation. Key collaborations include work on stochastic approximation, constrained diffusions, and reflected Brownian motion.

Teaching: Courses include Operations Research: Probabilistic Models, Information Theory, and Advanced topics in Probability and Stochastic Control.