ubsuny/PHY386
Feb 2026 – May 2026
4 Months active
Languages Used
MarkdownPythonJSONNone
Technical Skills
AI IntegrationAI integrationBasic algorithmsFile handlingJupyter NotebookMarkdown
owenabbata
PROFILE
Owenabbata
Developed a suite of interactive educational tools and scientific computing features for the ubsuny/PHY386 repository over four months, focusing on physics and data-driven analysis. Delivered Jupyter notebooks for circuit simulation, electrostatics, and astronomy, integrating Python-based algorithms and machine learning workflows. Enhanced project maintainability through codebase refactoring, configuration management, and test-driven practices. Implemented geospatial earthquake classification using scikit-learn and Cartopy, providing interpretable visualizations for risk assessment. Addressed code quality with bug fixes and documentation, supporting reproducible research and student engagement. Leveraged skills in Python, data analysis, and version control to create reusable, extensible content for physics education and analytics.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
26Total
Bugs
1
Commits
26
Features
10
Lines of code
9,767,199
Activity Months4
Your Network
18 peopleWork History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026: Delivered end-to-end Earthquake classification notebook for M5+ Ring of Fire (ubsuny/PHY386). The notebook uses k-NN and Random Forest in a geospatial context with latitude, longitude, depth, and magnitude features from USGS ComCat (2020–2024). Visualizations with Cartopy display decision regions on a world map, enabling intuitive risk assessment and exploratory analysis. This work culminated in a squash-merge final submission (commit 56287f5409226552fb2cdf8ae67bc5bcb23cad21). Overall impact: provides a reproducible, interpretable ML workflow for classifying significant earthquakes within the Ring of Fire, supporting risk evaluation and scientific insight. Technologies: Python, scikit-learn, Cartopy, Jupyter, geospatial data handling.
1 Commits • 1 Features
May 1, 2026May 2026: Delivered end-to-end Earthquake classification notebook for M5+ Ring of Fire (ubsuny/PHY386). The notebook uses k-NN and Random Forest in a geospatial context with latitude, longitude, depth, and magnitude features from USGS ComCat (2020–2024). Visualizations with Cartopy display decision regions on a world map, enabling intuitive risk assessment and exploratory analysis. This work culminated in a squash-merge final submission (commit 56287f5409226552fb2cdf8ae67bc5bcb23cad21). Overall impact: provides a reproducible, interpretable ML workflow for classifying significant earthquakes within the Ring of Fire, supporting risk evaluation and scientific insight. Technologies: Python, scikit-learn, Cartopy, Jupyter, geospatial data handling.
May 2026
April 2026
9 Commits • 3 Features
Apr 1, 2026April 2026 monthly summary for ubsuny/PHY386. Delivered two feature notebooks enabling hands-on learning in electromagnetism and astronomy, fixed a visualization bug to improve accuracy, and completed codebase cleanup to boost maintainability and future development velocity. Demonstrates strong value delivery with practical educational tools, data-driven ML experimentation, and improved code health.
April 2026
9 Commits • 3 Features
Apr 1, 2026April 2026 monthly summary for ubsuny/PHY386. Delivered two feature notebooks enabling hands-on learning in electromagnetism and astronomy, fixed a visualization bug to improve accuracy, and completed codebase cleanup to boost maintainability and future development velocity. Demonstrates strong value delivery with practical educational tools, data-driven ML experimentation, and improved code health.
March 2026
6 Commits • 3 Features
Mar 1, 2026During March 2026, delivered foundational project scaffolding for PHY386, streamlined test configuration to improve onboarding and ensure reproducible environments, expanded Homework 4 PDE heat conduction materials and notebooks to enhance instructional flow, and strengthened physics analytics with an advanced data analysis and visualization toolkit. These efforts improve maintainability, educational clarity, and data-driven decision-making, enabling faster contributor ramp-up and more robust classroom simulations. Tech work showcased Python environment configuration, test-driven practices, notebook organization for coursework, and advanced data visualization including curve fitting, residuals, histograms, and uncertainty reporting.
6 Commits • 3 Features
Mar 1, 2026During March 2026, delivered foundational project scaffolding for PHY386, streamlined test configuration to improve onboarding and ensure reproducible environments, expanded Homework 4 PDE heat conduction materials and notebooks to enhance instructional flow, and strengthened physics analytics with an advanced data analysis and visualization toolkit. These efforts improve maintainability, educational clarity, and data-driven decision-making, enabling faster contributor ramp-up and more robust classroom simulations. Tech work showcased Python environment configuration, test-driven practices, notebook organization for coursework, and advanced data visualization including curve fitting, residuals, histograms, and uncertainty reporting.
March 2026
February 2026
10 Commits • 3 Features
Feb 1, 2026February 2026 monthly performance for ubsuny/PHY386. Delivered scalable, interactive educational materials and robust circuit-analysis tooling, with notable AI-assisted capability exploration. Focused on producing business value through enhanced student engagement and reusable content while advancing engineering capabilities.
February 2026
10 Commits • 3 Features
Feb 1, 2026February 2026 monthly performance for ubsuny/PHY386. Delivered scalable, interactive educational materials and robust circuit-analysis tooling, with notable AI-assisted capability exploration. Focused on producing business value through enhanced student engagement and reusable content while advancing engineering capabilities.
Activity
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Quality Metrics
Correctness93.8%
Maintainability90.8%
Architecture90.0%
Performance90.8%
AI Usage42.2%
Skills & Technologies
Programming Languages
JSONMarkdownNonePython
Technical Skills
AI IntegrationAI integrationBasic algorithmsCode MaintenanceData AnalysisData analysisFile handlingGitJupyterJupyter NotebookJupyter NotebooksMarkdownPhysics EducationPythonPython development
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