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Jordan Perr-Sauer is a data science researcher in the Computation Science Center. He wants to use software to answer interesting scientific questions and to improve our world. Jordan is interested in performing research related to physics-informed machine learning, verification of machine leaning models, system identification, and uncertainty quantification. 

Research Interests

Software engineering

Data science and applied machine learning

Physics-informed machine learning

Uncertainty quantification

Verification and reproducibility of analysis results

Education

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

Professional Experience

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

Software Engineering Intern, FXall (2012)

REU Internship, Indiana University Bloomington (2011)

Featured Work

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)