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
Worked on the NNPDF/nnpdf repository to enhance theory-card parameterization and data integrity, focusing on improving modeling accuracy for particle physics analyses. Developed and integrated structured Monte Carlo variation cards for parameters such as Mcharm, mc_var, and alphas, enabling more precise uncertainty quantification. Ensured YAML schema correctness by updating the theory_id data type and standardized the ModEv field to EXA across all theory cards, reducing the risk of mis-parsing. Utilized YAML configuration management and data validation skills to stabilize the codebase, including rolling back provisional changes to maintain CI reliability and documenting updates for reproducibility in 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
52 peopleShared Repositories
25
achiefaMember
Giovanni De CrescenzoMember
Eva GroenendijkMember
Ella ColeMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eva Doortje Zee GroenendijkMember
Eleanor ColeMember
enoceraMember
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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline