Alexander Munoz
Scientist at Los Alamos National Laboratory
Santa Fe, New Mexico, United States
Summary
👤
Senior🎓
Top SchoolAlexander Munoz is a scientist and computational physicist with nine years of experience applying high-performance computing and machine learning to ab initio materials modeling and paleoclimatology. He has advanced DFT and quantum Monte Carlo workflows, contributed to PyQMC, and engineered datasets and equivariant neural networks for learning DFT densities at national labs. His work blends rigorous first-principles methods with practical ML tooling—hyperparameter studies, feature engineering, and experiment tracking with Weights & Biases—to turn physics data into predictive models. Based in Santa Fe, he brings domain depth from a PhD at UIUC and a pragmatic software skillset (Linux, Python, PyTorch) used to move research into reproducible, production-ready code.
9 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.), Physics, Bachelor of Science (B.S.), Physics at Arizona State UniversityUniversity of Illinois Urbana-Champaign
English, Spanish
Github Skills (7)
monte-carlo8
quantum-computing6
python5
javascript4
css4
foundation4
responsive4
Programming languages (2)
Github contributions (5)
armunoz/pyqmc
May 2019 - Oct 2019
Contributions:24 pushes, 1 branch in 5 months
armunoz/d18o_sss_region_id
Apr 2022 - Mar 2023
Contributions:1 release, 9 pushes in 11 months
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