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Hui Wang is an Associate Professor of Applied Mathematics at Brown University. His research focuses on the theory and applications of probabilistic methods, particularly in the context of game-theoretic importance sampling and Monte Carlo simulation. Wang's work addresses fundamental questions in mathematical finance, including option pricing and portfolio optimization, utilizing stochastic optimization techniques. His pivotal contribution includes the study of explicit solutions for path-dependent options under double exponential jump diffusion models, employing convex duality in portfolio optimization within incomplete semi-martingale markets. Continuing his research, Wang collaborates closely with Paul Dupuis to explore a game-theoretic approach to importance sampling. This approach connects algorithmic design with differential game principles, providing a framework for constructing efficient schemes. His research also extends to robust control in general queuing networks, aiming for comprehensive characterizations of optimal policies across various dimensionalities. Wang’s research endeavors have been supported by multiple NSF grants and he has authored numerous influential publications in leading journals. He also actively teaches courses related to statistical inference, computational probability, and Monte Carlo applications at Brown.
Department: Department of Economics