AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

• Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

• Research Scientist, Stochastic Numerics Research Group

uncertainty quantification numerical analysis stochastic differential equations Stochastic Optimal Control

• Research Scientist, Stochastic Numerics Research Group

numerical methods machine learning

• Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis uncertainty quantification optimal experimental design

• Postdoctoral Research Fellow, Applied Mathematics and Computational Science

numerical analysis

• Ph.D. Student, Applied Mathematics and Computational Science

• Ph.D. Student, Applied Mathematics and Computational Science

Monte carlo methods Bayesian computational statistics

• Postdoctoral Research Fellow, Stochastic Numerics Research Group