Sarah Hedengren
Adjunct Professor at Advanced Process Solutions, LLC
Provo, Utah, United States
Summary
👤
Senior🎓
Top SchoolSarah Hedengren is an engineer-entrepreneur and adjunct professor in Provo, Utah, with a decade of experience building software solutions for optimization and control across automation and semiconductor industries. She owns Advanced Process Solutions, developing practical optimization software, and contributes to the well-regarded open-source GEKKO project for machine learning and dynamic optimization, where she has implemented core solver improvements and performance fixes. As an engaging instructor in both lab and classroom settings at Brigham Young University, she translates complex control and mixed-integer optimization concepts into hands-on learning. Her background in electrical engineering and hands-on ASIC and RF work gives her a rare blend of hardware intuition and software systems expertise. An active mentor for women engineers and participant in STEM outreach, she balances technical depth with community-focused leadership.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science (BS), Electrical Engineering, Bachelor of Science (BS), Electrical Engineering at Brigham Young University
Stackoverflow
13,573reputation
444kreached
789answers
2questions
optimization
top-1%
python
top-1%
Github Skills (14)
python10
optimization10
linear-algebra9
numerical-optimization9
mixed-integer-programming8
cvxpy6
mathematical-optimization6
differential-equations6
ipopt6
nonlinear-optimization6
linear-programming6
gekko6
ode6
numpy6
Programming languages (5)
Github contributions (5)
BYU-PRISM/GEKKO
Mar 2018 - Jan 2023
GEKKO Python for Machine Learning and Dynamic Optimization
Role in this project:
Back-end Developer
Back-end DeveloperContributions:26 releases, 4 reviews, 476 commits in 4 years 11 months
Contributions summary:Sarah primarily contributed to the development and maintenance of the GEKKO Python library, focusing on adding new functionalities and fixing critical issues. Their work involved implementing a local solve executable, fixing errors related to the IMODE=3 setting, and updating the APM binary to improve performance and resolve issues. The user's commits focused on the core components of the library as well as the associated binaries.
pythonnonlinear-optimizationoptimizationmachine-learningconstrained-optimization
APMonitor/mdc
Aug 2021 - Aug 2022
Matlab Dynamics and Control
Contributions:1 release, 61 commits, 1 PR in 1 year
roboticscomplexitymatlabstructural-engineeringdiscrete-event-simulation
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