blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Code CleanupCode RefactoringData VisualizationMachine Learning AnalysisNumerical ComputingNumerical Optimization
Davide Breton
PROFILE
Davide Breton
During June 2025, Davide Breton developed advanced statistical modeling and data visualization features for the blackSwanCS/Higgs_collaboration_B repository. He implemented systematics-aware negative log-likelihood modeling with nuisance parameter fitting and mu estimation, enabling robust inference under systematic uncertainties. Davide also streamlined the Asymptotic Most Powerful (AMS) statistic workflow by optimizing threshold scanning and removing redundant code, which improved runtime and workflow clarity. Additionally, he created a plotting utility to visualize score distributions for signal and background, supporting data-driven decision making in physics analysis. His work leveraged Python, scientific computing, and statistical analysis, demonstrating depth in both numerical optimization and code maintainability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
3
Lines of code
160
Activity Months1
Your Network
21 people
Work History
June 2025
7 Commits • 3 Features
Jun 1, 2025June 2025 monthly performance summary for blackSwanCS/Higgs_collaboration_B focusing on advancing statistical modeling and AMS-based discovery analytics, with implemented features delivering robust systematics-aware inference and improved visualization for score distributions. Highlights include NLL modeling with nuisance parameter fits under systematics, AMS workflow improvements, and plotting utilities to support data-driven decision making in physics analyses.
7 Commits • 3 Features
Jun 1, 2025June 2025 monthly performance summary for blackSwanCS/Higgs_collaboration_B focusing on advancing statistical modeling and AMS-based discovery analytics, with implemented features delivering robust systematics-aware inference and improved visualization for score distributions. Highlights include NLL modeling with nuisance parameter fits under systematics, AMS workflow improvements, and plotting utilities to support data-driven decision making in physics analyses.
June 2025
Activity
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Quality Metrics
Correctness82.8%
Maintainability82.8%
Architecture80.0%
Performance65.8%
AI Usage22.8%
Skills & Technologies
Programming Languages
Python
Technical Skills
Code CleanupCode RefactoringData VisualizationMachine Learning AnalysisNumerical ComputingNumerical OptimizationPhysics AnalysisPythonScientific ComputingStatistical Analysis
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