blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Code RefactoringData AnalysisData VisualizationMachine LearningMatplotlibPandas
Quenard Stanislas
PROFILE
Quenard Stanislas
Stanislas Quenard developed foundational visualization and uncertainty analysis tools for the Higgs dataset within the blackSwanCS/Higgs_collaboration_B repository. He implemented a data visualization and testing pipeline using Python, Pandas, and Matplotlib, enabling pair-plot visualizations of key dataset features and systematic uncertainty testing through pseudo-experiments. Stanislas also introduced TES and JES uncertainty analysis utilities, supporting per-bin variation analysis, histogram generation, and optional background plotting, while establishing robust data ingestion and test configurations. His work included refactoring legacy analysis code and removing obsolete assets, resulting in improved maintainability and reproducibility for scientific computing workflows in the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
6,282
Activity Months1
Your Network
41 people
Work History
June 2025
7 Commits • 2 Features
Jun 1, 2025June 2025: Delivered foundational visualization and uncertainty-analysis capabilities for the Higgs dataset project. Implemented an integrated visualization and testing pipeline and launched TES/JES uncertainty analysis utilities, with initial data ingestion, robust test configurations, and groundwork for per-bin uncertainty quantification. Cleaned up legacy analysis code and assets to improve maintainability and reproducibility across experiments.
7 Commits • 2 Features
Jun 1, 2025June 2025: Delivered foundational visualization and uncertainty-analysis capabilities for the Higgs dataset project. Implemented an integrated visualization and testing pipeline and launched TES/JES uncertainty analysis utilities, with initial data ingestion, robust test configurations, and groundwork for per-bin uncertainty quantification. Cleaned up legacy analysis code and assets to improve maintainability and reproducibility across experiments.
June 2025
Activity
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Quality Metrics
Correctness85.8%
Maintainability85.8%
Architecture82.8%
Performance80.0%
AI Usage25.8%
Skills & Technologies
Programming Languages
Python
Technical Skills
Code RefactoringData AnalysisData VisualizationMachine LearningMatplotlibPandasPlottingPythonScientific Computing
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