معرفی
Hui Wang is an Associate Professor of Applied Mathematics at Brown University, specializing in stochastic optimization, mathematical finance, and game-theoretic importance sampling. He holds a PhD from Columbia University (2000) and an MS from Tsinghua University (1996). His research focuses on variance reduction techniques in Monte Carlo simulations, particularly through a game-theoretic lens developed with Paul Dupuis. He has received the Manning Assistant Professorship (2004) and held grants from NSF and DOE.
Teaching interests include probability theory, stochastic processes, and financial mathematics courses such as APMA 1720 and APMA 1655. His work intersects with queuing networks, simulation methodologies, and stochastic games. Recent efforts explore robust control in high-dimensional systems and extensions of importance sampling to complex network models. He actively referees for top journals like Annals of Applied Probability and SIAM Journal on Control and Optimization.
Key contributions include explicit solutions for path-dependent options and convex duality methods in incomplete markets. Current research priorities involve advancing differential game frameworks for adaptive sampling algorithms and addressing scalability challenges in stochastic network optimization.
