Jordan Perr-Sauer is a researcher in the Computational Science Center interested in software sustainability and explainable, verifiable, and interpretable artificial intelligence. He has worked on projects across multiple domains at NREL, including wind energy, transportation, advanced manufacturing, and high-performance computing. Jordan has a bachelor’s degree in applied mathematics, a master’s degree in computer science, and aims to use software to answer interesting scientific questions and to improve our world.

Research Interests

Explainable, interpretable, and verifiable artificial intelligence

Software engineering

Data science and applied machine learning

Physics-informed machine learning

Uncertainty quantification

Verification and reproducibility of analysis results

Education

M.S., Computer Science, University of Colorado, Boulder

B.S., Applied Mathematics, University of Colorado, Denver

Professional Experience

Technical Co-Founder, Moby Systems, Inc (2011–2014)

REU Internship, Indiana University Bloomington (2011)

Featured Work

Publications

OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms, The Journal of Open Source Software (2020)

Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data, NREL Technical Report (2020)

Short-Term Wind Forecasting Using Statistical Models with a Fully Observable Wind Flow, Journal of Physics: Conference Series (2020)

Open-Source Software

OpenOA: Open Operational Assessment

ENTR Runtime (GitHub)

BUTTER-Clarifier (GitHub)

Data Sets

BUTTER: Better Understanding of Training Topologies Through Empirical Results (GitHub)