There is a growing need for innovation in nuclear detection, nuclear security and nonproliferation, and nuclear energy to meet our goals in global security, economic competitiveness, and environmental responsibility. There are incredible opportunities for progress in these important areas enabled by exascale computing and data analysis; however, we need to discover how to effectively capitalize on these opportunities. This talk will touch upon three major challenges: performing computations on exascale architectures effectively and reproducibly, having sufficiently accurate data for the highly resolved solutions we need, and performing analysis on such large datasets efficiently.
Rachel Slaybaugh is an Assistant Professor of Nuclear Engineering at the University of California, Berkeley. Slaybaugh researches computational methods applied to nuclear reactors, nuclear non-proliferation and security, and shielding. She recently served as a Program Director at the Advanced Research Projects Agency-Energy (ARPA-E) and has served on the Nuclear Energy Advisory Committee (NEAC). She is the founding Board Chair for Good Energy Collective, and a Senior Fellow at the Breakthrough Institute. Prof. Slaybaugh is developing programs to train and inspire the next generation to innovate in clean energy, including the Nuclear Innovation Bootcamp, and also focuses on improving transparency and reproducibility in computational science and scientific publication.