Pezhman Zarabadi-poor
Principal Data Engineer
Oxford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolPezhman Zarabadi-poor is a Principal Data Engineer based in Oxford with nine years of experience applying computational methods to materials and energy challenges at the intersection of experimental science and data engineering. Trained as an inorganic chemist (PhD, University of Tehran) and seasoned through postdoctoral and research roles at institutions like Oxford, Bath, and The Faraday Institution, he uniquely blends hands-on lab experience with production-grade data systems. At Elysia he leads data engineering for battery intelligence, translating complex materials outputs into reliable pipelines and models. He is an active contributor to the widely used pymatgen project, where his back-end work improved parsing of Lobster outputs and maintained backward compatibility—evidence of both domain expertise and careful software craftsmanship. Known for thriving in collaborative, multi-institution projects, he pivoted into computational work through self-driven learning after his PhD, bringing curiosity and practical problem-solving to cross-disciplinary teams.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Applied Science (B.A.Sc.), Chemistry, Bachelor of Applied Science (B.A.Sc.), Chemistry at Bu-Ali Sina UniversityDoctor of Philosophy (PhD), Inorganic Chemistry, Doctor of Philosophy (PhD), Inorganic Chemistry at University of Tehran
Github Skills (7)
unit-testing10
pymatgen10
science10
unit-test10
python10
materials-informatics10
testing10
Programming languages (7)
Github contributions (5)
materialsproject/pymatgen
Sep 2021 - Sep 2021
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 1 PR, 7 comments in 6 days
Contributions summary:Pezhman primarily focused on modifying the `pymatgen/io/lobster` module, specifically the `outputs.py` file, indicating expertise in processing Lobster output files. Their contributions involved fixing parsing issues, ensuring backward compatibility with older Lobster versions, and updating related tests. These changes likely improved the reliability and maintainability of the code within the materials science context of the project.
moleculespythonscienceelectronic-structurepowers
ezpzbz/aiida-catmat
Jun 2020 - Jul 2022
Collection of AiiDA WorkChains Developed in CATMAT project
Contributions:1 release, 153 commits, 2 PRs in 2 years 1 month
aiida
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