JeffersonLab/coatjava
Jan 2025 – Apr 2025
4 Months active
Languages Used
Java
Technical Skills
AI DevelopmentAI Model IntegrationAlgorithm DesignAlgorithm RefinementConfiguration ManagementData Analysis
MathieuOuillon
PROFILE
Mathieuouillon
Mathieu Ouillon contributed to JeffersonLab/coatjava by developing and refining AI-assisted workflows for detector data analysis over a four-month period. He integrated AI models using Java and the Deep Java Library, standardizing model asset management and improving dependency handling for reliable deployment. Mathieu enhanced algorithmic efficiency in track candidate generation and optimized inference environments for consistent performance. He addressed geometry alignment issues by implementing coordinate frame corrections, improving hit-position accuracy and validation reliability. His work demonstrated depth in AI/ML model integration, algorithm optimization, and detector physics, resulting in more maintainable code and robust data processing pipelines for physics analysis.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
14Total
Bugs
3
Commits
14
Features
6
Lines of code
590
Activity Months4
Your Network
19 people
Work History
April 2025
2 Commits
Apr 1, 2025April 2025 monthly summary for JeffersonLab/coatjava focusing on detector geometry corrections and validation improvements that enhance hit-position accuracy and data quality. The changes align hit coordinates with the actual spatial arrangement and ensure wire cell definitions are CCW for concave component validation within ALERT, improving validation reliability and downstream physics analyses.
2 Commits
Apr 1, 2025April 2025 monthly summary for JeffersonLab/coatjava focusing on detector geometry corrections and validation improvements that enhance hit-position accuracy and data quality. The changes align hit coordinates with the actual spatial arrangement and ensure wire cell definitions are CCW for concave component validation within ALERT, improving validation reliability and downstream physics analyses.
April 2025
March 2025
4 Commits • 3 Features
Mar 1, 2025Concise monthly summary for 2025-03 focusing on key accomplishments, major impact, and technologies demonstrated for JeffersonLab/coatjava. No critical bug fixes reported this period; emphasis on reliability, performance, and scalable analysis.
March 2025
4 Commits • 3 Features
Mar 1, 2025Concise monthly summary for 2025-03 focusing on key accomplishments, major impact, and technologies demonstrated for JeffersonLab/coatjava. No critical bug fixes reported this period; emphasis on reliability, performance, and scalable analysis.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for JeffersonLab/coatjava focusing on reliability improvements and dependency modernization. Key changes include a bug fix for the AI model loading path with dynamic resource path resolution and integration of the updated GEMC geometry model, plus a DJL library upgrade from 0.31.1 to 0.32.0 to improve stability and compatibility with the latest geometry updates.
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for JeffersonLab/coatjava focusing on reliability improvements and dependency modernization. Key changes include a bug fix for the AI model loading path with dynamic resource path resolution and integration of the updated GEMC geometry model, plus a DJL library upgrade from 0.31.1 to 0.32.0 to improve stability and compatibility with the latest geometry updates.
February 2025
January 2025
6 Commits • 2 Features
Jan 1, 2025January 2025 performance: Delivered robust AI integration and data handling with explicit PyTorch dependencies, standardized model asset paths, and a dedicated Model class; migrated model location to etc/nnet and enabled path resolution via CLASResources. Strengthened data processing with PreClustering robustness, including border-case handling and a separate Hit phi comparator for readability. Fixed a critical phi calculation bug by correcting Math.atan2 argument order in Track.java. These efforts improve reliability, maintainability, and time-to-delivery for model-assisted workflows.
January 2025
6 Commits • 2 Features
Jan 1, 2025January 2025 performance: Delivered robust AI integration and data handling with explicit PyTorch dependencies, standardized model asset paths, and a dedicated Model class; migrated model location to etc/nnet and enabled path resolution via CLASResources. Strengthened data processing with PreClustering robustness, including border-case handling and a separate Hit phi comparator for readability. Fixed a critical phi calculation bug by correcting Math.atan2 argument order in Track.java. These efforts improve reliability, maintainability, and time-to-delivery for model-assisted workflows.
Activity
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Quality Metrics
Correctness88.6%
Maintainability90.0%
Architecture87.2%
Performance80.0%
AI Usage28.6%
Skills & Technologies
Programming Languages
Java
Technical Skills
AI DevelopmentAI Model IntegrationAI/MLAI/ML Model IntegrationAlgorithm DesignAlgorithm DevelopmentAlgorithm OptimizationAlgorithm RefinementConfigurationConfiguration ManagementCoordinate SystemsData AnalysisData StructuresDependency ManagementDetector Physics
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