Sobes’ research covers a broad spectrum of reactor physics. Research interests currently span four major areas from nuclear physics to reactor design. With a background in nuclear data, he continues to work on problems in the nuclear data pipeline with a particular interest in the application of Artificial Intelligence (AI)/ Machine Learning (ML) algorithms.Further research projects look at improving radiation transport calculations and how to accelerate simulations of nuclear systems on modern heterogeneous computing architectures (HPC). Research in advanced and traditional nuclear reactor analysis, including Sensitivity/Uncertainty (S/U) analysis methods, continues to be of interest. Developing algorithms for autonomous design of nuclear systems is a new research area. Last but not least, the entire research portfolio is applied to the design and future operation of the Fast Neutron Source experimental facility proposed to be built on the UT campus.
For an up-to-date list of publications and professional activities please see Sobes' CV here.
Massachusetts Institute of Technology, PhD Nuclear Science and Engineering, Feb. 2011–Sept. 2013
Thesis: Coupled Differential and Integral Data Analysis for Improved Uncertainty Quantification of the Cu-63,65 Cross Section Evaluations.
Massachusetts Institute of Technology, BS Nuclear Science and Engineering, Sept. 2007–Feb. 2011
Thesis: Individual Pebble Temperature Peaking Factor due to Local Pebble Arrangement in a Pebble Bed Reactor Core.