Speaker(s)
Kathryn D. Huff
Katy Huff was a Nuclear Science and Security Consortium postdoctoral scholar in the Nuclear Engineering Department and a BIDS Data Science Fellow. In 2013, she received her PhD in Nuclear Engineering from the University of Wisconsin, Madison. She also holds a bachelor's degree in Physics from the University of Chicago. Katy is currently an Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering and and a Blue Waters Assistant Professor with the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.
Her research includes computational nuclear fuel cycle analysis and computational simulation of coupled, transient, nuclear reactor physics. Improving the safety and sustainability of nuclear power requires improved nuclear reactor designs, fuel cycle strategies, and waste-disposal concepts. The systems are sufficiently complex that breakthrough advancements may emerge when modern data methodologies are applied to their simulation. In particular, faithful assessments of nuclear reactor response in hypothetical accident scenarios require high-fidelity, tightly coupled, time-dependent simulation of neutronics, thermal hydraulics, and structural performance.
She is also passionate about scientific computing education and sharing best practices for reproducible science. Her collaboration with BIDS seeks to enrich open source nuclear science and engineering toolkits, scientific computing education, and best practices for reproducibility and transparency in the current scientific landscape.
In addition to her current work, she has previously participated in varied research, including experimental cosmological astrophysics and experimental non-equilibrium granular material phase dynamics. She was a founding member of The Hacker Within scientific computing group in 2009 and has been a volunteer instructor for Software Carpentry since that time. Among other professional service, she is active in the American Nuclear Society and the Scientific Computing with Python (SciPy) community.