blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisMachine LearningRefactoringScientific Computing
SamGherbi
PROFILE
Samgherbi
During June 2025, Gherbi developed a reusable uncertainty quantification framework for the blackSwanCS/Higgs_collaboration_B repository, targeting systematic risk assessment in Higgs machine learning analyses. Using Python and leveraging skills in data analysis and scientific computing, Gherbi implemented scripts to load datasets, apply TES and JES systematic variations, and generate score histograms, with placeholder fit routines to support future expansion. The framework enables scalable, user-facing uncertainty analysis, supporting early decision-making in complex workflows. Gherbi also improved code clarity by renaming key files to reflect their intended use, maintaining clear commit history and demonstrating careful attention to code hygiene and maintainability throughout.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
110
Activity Months1
Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary: Focused on building a reusable uncertainty quantification capability for Higgs ML analyses in blackSwanCS/Higgs_collaboration_B. Delivered a Python-based framework to load data, apply systematic variations (TES and JES), produce score histograms, and support placeholder fit routines, enabling early risk assessment and better decision-making in analysis workflows. This work lays the foundation for scalable, user-facing uncertainty analysis across Higgs analyses.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary: Focused on building a reusable uncertainty quantification capability for Higgs ML analyses in blackSwanCS/Higgs_collaboration_B. Delivered a Python-based framework to load data, apply systematic variations (TES and JES), produce score histograms, and support placeholder fit routines, enabling early risk assessment and better decision-making in analysis workflows. This work lays the foundation for scalable, user-facing uncertainty analysis across Higgs analyses.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisMachine LearningRefactoringScientific Computing
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