Ryan will cover a variety of research topics being investigated in his group at Notre Dame, including using data-driven models to estimate the time-dependent behavior of fission experiments, the use of compressed sensing to estimate Monte Carlo solutions, and the application of machine learning to improve nuclear data. This talk will highlight how knowledge from statistics, applied mathematics, and computer science can be used to increase the impact of research in nuclear engineering applications. The talk will conclude with future research opportunities in these areas.

Ryan McClarren is a graduate of the University of Michigan NERS program with BSE, MSE, and PhD degrees. Currently he serves as Associate Professor of Aerospace and Mechanical Engineering at the University of Notre Dame. McClarren joined the Notre Dame faculty in August 2017. His research interests include the application of machine learning and compressed sensing to numerical simulation, numerical methods for X-ray radiative transfer and particle transport and uncertainty quantification. He received the 2019 Young Member’s Research Award by the Mathematics and Computations Division (MCD) of the American Nuclear Society (ANS).

He is the author of two textbooks: the recently published Uncertainty Quantification and Predictive Computational Science, a textbook focused on senior undergraduate and early-career graduate students in engineering and the physical sciences, and Computational Nuclear Engineering and Radiological Science Using Python, a textbook for undergraduate engineering students that uses the Python programming language to present more easily accessible numerical methods for nuclear energy, radiation protection and homeland security applications.