ubsuny/PHY386
Feb 2025 – May 2025
4 Months active
Languages Used
Jupyter NotebookLatexMATLABMarkdownPythonCSV
Technical Skills
Computational PhysicsData AnalysisData ScienceGoogle ColabJupyter NotebookJupyter Notebooks
EODAngelo
PROFILE
Eodangelo
Over four months, Angelo contributed to the ubsuny/PHY386 repository by developing a suite of Jupyter Notebook-based features for physics and astrophysics coursework. He engineered data analysis pipelines and machine learning workflows in Python, leveraging libraries such as NumPy, TensorFlow, and Astropy to process experimental and astronomical datasets. His work included implementing quantum mechanics simulations, image processing for lunar crater analysis, and CNN-based spectral classification, all with a focus on reproducibility and clear documentation. Angelo also improved project structure and data organization, ensuring that course materials were portable, maintainable, and supported hands-on learning with real-world scientific data.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
21Total
Bugs
2
Commits
21
Features
10
Lines of code
13,325
Activity Months4
Your Network
11 people
Work History
May 2025
3 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05: Delivered a feature-rich Lunar crater size distribution analysis pipeline with enhanced visualization, enabling more accurate surface age estimation and insightful crater distribution discussions. Also completed notebook cleanup to improve reproducibility and reduce noise.
3 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05: Delivered a feature-rich Lunar crater size distribution analysis pipeline with enhanced visualization, enabling more accurate surface age estimation and insightful crater distribution discussions. Also completed notebook cleanup to improve reproducibility and reduce noise.
May 2025
April 2025
8 Commits • 5 Features
Apr 1, 2025April 2025 performance summary for ubsuny/PHY386. Delivered a suite of notebook-based coursework features, improved data processing pipelines, and introduced CNN-based spectral classification, elevating both teaching resources and technical capabilities. Key design focus was on reproducibility, GPU-accelerated workflows, and clear traceability of deliverables to business value.
April 2025
8 Commits • 5 Features
Apr 1, 2025April 2025 performance summary for ubsuny/PHY386. Delivered a suite of notebook-based coursework features, improved data processing pipelines, and introduced CNN-based spectral classification, elevating both teaching resources and technical capabilities. Key design focus was on reproducibility, GPU-accelerated workflows, and clear traceability of deliverables to business value.
March 2025
7 Commits • 3 Features
Mar 1, 2025Professional monthly summary for 2025-03 focused on business value, technical achievements, and reproducibility for the PHY386 workstream.
7 Commits • 3 Features
Mar 1, 2025Professional monthly summary for 2025-03 focused on business value, technical achievements, and reproducibility for the PHY386 workstream.
March 2025
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for ubsuny/PHY386. Key feature delivered: Physics course HW1 notebooks — a new notebook with content expansion; repository improvements and maintainability. No major bugs fixed this month. Business impact: enhanced course readiness, portability across environments (including Google Colab), and scalable content for students with clear learning objectives. Technologies/skills demonstrated: Python, MATLAB, LaTeX, Jupyter notebooks, markdown structure, conditional logic, loops, functions, data structures (lists, dictionaries), file I/O, and documentation via learning objectives and citations.
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for ubsuny/PHY386. Key feature delivered: Physics course HW1 notebooks — a new notebook with content expansion; repository improvements and maintainability. No major bugs fixed this month. Business impact: enhanced course readiness, portability across environments (including Google Colab), and scalable content for students with clear learning objectives. Technologies/skills demonstrated: Python, MATLAB, LaTeX, Jupyter notebooks, markdown structure, conditional logic, loops, functions, data structures (lists, dictionaries), file I/O, and documentation via learning objectives and citations.
Activity
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Quality Metrics
Correctness82.0%
Maintainability81.0%
Architecture78.2%
Performance76.2%
AI Usage26.6%
Skills & Technologies
Programming Languages
CSVJupyter NotebookLatexMATLABMarkdownPython
Technical Skills
AI-Assisted DevelopmentAstrophysicsAstropyComputational PhysicsComputer VisionData AnalysisData CollectionData EngineeringData ScienceData VisualizationDeep LearningDocumentationFile ManagementGoogle ColabImage Processing
Repositories Contributed To
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