blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisMachine LearningRefactoringScientific Computing
SamGherbi
PROFILE
Samgherbi
Developed a reusable uncertainty quantification framework for Higgs machine learning analyses in the blackSwanCS/Higgs_collaboration_B repository, focusing on systematic risk assessment in scientific computing workflows. The solution, implemented in Python, enables data loading, application of TES and JES systematic variations, and generation of score histograms, with placeholder fit routines to support future analysis extensions. This work established a scalable, user-facing approach for quantifying and comparing uncertainties in Higgs analyses, supporting better decision-making. Additionally, maintained code clarity through targeted refactoring, including renaming scripts to reflect intended use, and ensured clear commit history, demonstrating strong skills in data analysis and machine learning.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
110
Activity Months1
Your Network
21 peopleShared Repositories
21
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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