Rate My Professor Ryan McClarren

McClarren's research centers on particle transport for neutrons and x-rays, uncertainty quantification, high-energy density physics, high-performance computing, machine learning, and predictive computational science. His group develops algorithms for exascale supercomputers, uncertainty quantification methods for simulations, and machine learning enhancements for radiative transfer and neutron transport in fission and inertial confinement fusion systems. He has authored key textbooks including Uncertainty Quantification and Predictive Computational Science (Springer International Publishing, 2018) and Computational Nuclear Engineering and Radiological Science Using Python (Academic Press, 2017). Selected publications encompass "Robust and accurate filtered spherical harmonics expansions for radiative transfer" (Journal of Computational Physics, 2010), "Calculating time eigenvalues of the neutron transport equation with dynamic mode decomposition" (Nuclear Science and Engineering, 2019), and "Self-similar solutions for high-energy density radiative transfer with separate ion and electron temperatures" (Proceedings of the Royal Society A, 2021). His publications appear in engineering, physics, mathematics, and statistics journals. McClarren received the American Nuclear Society Young Member Research Award from the Mathematics and Computations Division in 2019 and serves as deputy director of the U.S. Department of Energy-funded Center for Exascale Monte Carlo Neutron Transport. He is a member of the American Nuclear Society and the Society of Industrial and Applied Mathematics.