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About Xiaoliang
Xiaoliang Wan is a Professor in the Department of Mathematics and the Center for Computation and Technology at Louisiana State University, where he has served since 2008, advancing to full professor in 2021 after positions as Associate Professor from 2015 to 2021 and Assistant Professor from 2009 to 2015. He previously held a Visiting Assistant Professor role at LSU in 2008-2009, a Postdoctoral Research Associate position at Princeton University's Program in Applied and Computational Mathematics in 2008-2009, and a Joint Postdoctoral Research Associate position at Brown University and MIT in 2007-2008. Wan earned his PhD from Brown University in 2007, MS from Peking University in 2001, and BS from Peking University in 1998.
His research interests include scientific machine learning, stochastic modeling, numerical methods for stochastic partial differential equations, adaptivity in numerical simulations, and the minimum action method for large deviation principles. Wan has published extensively in journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and Nonlinearity. Key publications feature 'An adaptive multi-element generalized polynomial chaos method for stochastic differential equations' (2005), 'Multi-element generalized polynomial chaos for arbitrary probability measures' (2006), 'DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations' (2023), 'A minimum action method for dynamical systems with constant time delays' (2021, SIAM Journal on Scientific Computing), 'A minimum action method with optimal linear time scaling' (2015, Communications in Computational Physics), and 'Model the nonlinear instability of wall-bounded shear flows as a rare event: A study on two-dimensional Poiseuille flow' (2015, Nonlinearity). He has been principal investigator on grants from the National Science Foundation, Department of Energy, and Air Force Office of Scientific Research, and received the LSU Council on Research Summer Stipend Program award in 2010. Wan's contributions have advanced uncertainty quantification, rare event simulations, and computational methods for stochastic systems.
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