Robert Parker
Scientist at Los Alamos National Laboratory
Los Alamos, New Mexico, United States
Summary
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Rockstar🎓
Top SchoolRobert Parker is a scientist and optimization specialist with seven years of experience applying mathematical optimization and nonlinear algorithms to design and operational problems in the chemical and electric power industries. With a Ph.D. in Chemical Engineering from Carnegie Mellon and a track record at Los Alamos and Sandia National Laboratories, he develops algorithms for power-grid operation and builds tooling that improves solver robustness and model debugging. He’s an active contributor to Pyomo, enhancing its configuration system and indexing/constraint handling—work that directly improves large-scale algebraic modeling workflows. Comfortable bridging theory and production, he pairs deep numerical expertise with practical Python engineering and an eye for robustness in complex, constrained systems.
7 years of coding experience
3 years of employment as a software developer
High School Diploma, High School Diploma at Poway High SchoolDoctor of Philosophy (Ph.D.), Chemical Engineering, Doctor of Philosophy (Ph.D.), Chemical Engineering at Carnegie Mellon UniversityB.S., Chemical Engineering, B.S., Chemical Engineering at University of California, Los Angeles
English
Github Skills (9)
pyomo10
python10
computer-engineering9
lang8
linear-programming8
data-structures8
modeling8
data-structure8
mathematical-programming8
Programming languages (8)
Github contributions (5)
Pyomo/pyomo
Feb 2020 - Dec 2022
An object-oriented algebraic modeling language in Python for structured optimization problems.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:217 reviews, 1075 commits, 100 PRs in 2 years 10 months
Contributions summary:Robert's commits focused on improving the Pyomo configuration system, including the introduction of a not-specified class and handling of multiple variable and constraint indexing. These changes involved refinements to the core configuration system, contributing to efficiency improvements, and improving the handling of slice functionalities. The contributions encompass changes to the Python codebase, directly impacting how the Pyomo framework handles and processes mathematical models and their components.
pythonnonlinear-programmingstructuredgurobiproblems
Robbybp/surrogate-vs-implicit
Apr 2024 - Feb 2025
Comparing surrogate models and implicit function formulations for chemical process models
Contributions:6 reviews, 4 PRs, 10 pushes in 10 months
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