Natalie Isenberg is a data scientist and computational research scientist at Pacific Northwest National Laboratory with 11 years of experience in scalable numerical optimization and decision-making under uncertainty. She earned a Ph.D. in Chemical Engineering from Carnegie Mellon and was the Amalie Emmy Noether Postdoctoral Fellow in Applied Mathematics at Brookhaven National Laboratory. Her work bridges applied math and engineering, developing robust optimization algorithms and practical solver implementations. She is an active open-source contributor to Pyomo, where she implemented the PyROS solver and the GRCS algorithm to support robust optimization workflows. Known for translating research-grade methods into production-ready code, she excels at making complex optimization approaches scalable and usable. Based in the United States, she combines deep theoretical training with hands-on software development across scientific computing projects.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Chemical Engineering, Bachelor of Science - BS, Chemical Engineering at University of Pittsburgh Swanson School of Engineering
An object-oriented algebraic modeling language in Python for structured optimization problems.
Role in this project:
Back-end Developer
Contributions:52 reviews, 66 commits, 2 PRs in 2 months
Contributions summary:Natalie's commits primarily involve the development of the PyROS solver within the Pyomo framework. The user added new modules to support robust optimization, implemented the core GRCS algorithm, and modified existing utility functions. These contributions are evidenced by the addition of new files and changes to existing code related to optimization algorithms and related utilities.
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