NNPDF/nnpdf
Nov 2025 – Nov 2025
1 Month active
Languages Used
YAML
Technical Skills
YAML configuration managementconfiguration managementdata analysisdata managementdata modelingdata validation
Andrew Pietraszkiewicz
PROFILE
Andrew Pietraszkiewicz
During November 2025, Andrzej Pietraszkiewicz enhanced the NNPDF/nnpdf repository by developing structured MC variation theory cards for parameters such as Mcharm, mc_var, and alphas, aiming to improve modeling precision and uncertainty quantification in particle physics analyses. He ensured data integrity by correcting the theory_id data type in YAML files and standardized the ModEv field to EXA, supporting consistent configuration management. Andrzej utilized YAML and data validation skills to stabilize the codebase, reverting provisional changes when necessary to maintain CI reliability. His work demonstrated careful attention to schema correctness and reproducibility, contributing to more robust scientific computing workflows.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
2
Lines of code
2,492
Activity Months1
Your Network
50 people
Work History
November 2025
11 Commits • 2 Features
Nov 1, 2025November 2025 focused on strengthening NNPDF theory-card parameterization and data integrity to improve modeling accuracy and reliability of fits. Implemented structured MC variations for Mcharm, mc_var, and alphas to enable tighter uncertainty quantification; ensured YAML schema correctness by fixing theory_id data types; standardized ModEv field to EXA across cards; stabilized codebase by reverting provisional MC variation cards when needed, and documented changes for downstream reproducibility. These steps collectively enhance modeling precision, reduce risk of mis-parsing theory cards, and support more robust physics analyses.
11 Commits • 2 Features
Nov 1, 2025November 2025 focused on strengthening NNPDF theory-card parameterization and data integrity to improve modeling accuracy and reliability of fits. Implemented structured MC variations for Mcharm, mc_var, and alphas to enable tighter uncertainty quantification; ensured YAML schema correctness by fixing theory_id data types; standardized ModEv field to EXA across cards; stabilized codebase by reverting provisional MC variation cards when needed, and documented changes for downstream reproducibility. These steps collectively enhance modeling precision, reduce risk of mis-parsing theory cards, and support more robust physics analyses.
November 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability98.2%
Architecture98.2%
Performance98.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
YAML
Technical Skills
YAML configuration managementconfiguration managementdata analysisdata managementdata modelingdata validationmodelingnumerical analysisparticle physicsscientific computingtheoretical physics
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