Adrian Maldonado
Assistant Energy Systems Scientist at Argonne National Laboratory
Chicago, Illinois, United States
Summary
👤
Senior🎓
Top SchoolAdrian Maldonado is an Assistant Energy Systems Scientist at Argonne National Laboratory with 11 years of experience applying computational methods to power systems and electricity markets. He holds a Ph.D. in Electrical and Computer Engineering and a master's in Electricity Markets from Illinois Institute of Technology, grounding his work at the intersection of research and applied engineering. At Argonne he focuses on performance-driven solutions for energy systems, translating complex models into efficient, robust implementations. As a contributor to the PETSc project he enhanced DMNetwork examples, improving matrix assembly performance, fixing memory leaks, and adding scalable random-network generation—work that underscores his attention to numerical efficiency and software reliability. Based in Chicago, he blends academic rigor with hands-on code optimization, making him adept at taking algorithms from prototype to production-ready tools. Colleagues value his ability to spot subtle performance bottlenecks and refactor large scientific codebases for real-world energy applications.
10 years of coding experience
Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering at Illinois Institute of TechnologyLicense, Industrial Engineering, License, Industrial Engineering at Universidad Politécnica de Valencia
English, Spanish
Github Skills (7)
c1710
petsc10
c1110
k10
performance-optimization9
linear-algebra8
parallel-computing8
Programming languages (3)
Github contributions (5)
petsc/petsc
Aug 2016 - May 2017
Mirror of https://gitlab.com/petsc/petsc
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:22 commits in 8 months
Contributions summary:Adrian primarily focused on enhancing the DMNetwork example within the PETSc library. They added a new example (ex2) with a random network generator, allowing for the creation of networks of varying sizes. Their work involved code refactoring, optimization of matrix assembly within the DMNetwork framework, and improvements to the matrix creation process for performance gains. The user also fixed memory leaks and other issues, further contributing to the robustness of the example code.
petscgitlab
lapreconditioners-dev/LSPreconditioners.jl
Jul 2023 - Sep 2023
Linear System Preconditioners
Contributions:10 reviews, 6 PRs, 7 pushes in 2 months
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