Amanda Muyskens

Amanda Muyskens is a staff member in the Applied Statistic Group at LLNL, where she leads the MuyGPs project that has developed novel methods for scalable, nonstationary Gaussian processes for high-performance computing and the Data Science Summer Institute. Her expertise includes surrogate modeling, Gaussian process models, computationally efficient ML, uncertainty quantification, and statistical consulting. Dr. Muyskens holds bachelor’s degrees in mathematics and music performance from the University of Cincinnati and an M.S. and Ph.D. in statistics from North Carolina State University.