
Over four months, contributed to the ubsuny/PHY386 repository by building educational data analysis frameworks, reproducible Colab workflows, and automated machine learning pipelines for physics coursework. Developed Jupyter Notebook modules for beam theory, stellar classification, and exoplanet candidate vetting, leveraging Python, pandas, and scikit-learn for data cleaning, visualization, and model training. Established project scaffolding and templates to streamline onboarding and collaboration, while maintaining repository integrity through regular cleanup and bug fixes. Delivered classroom-ready materials and automated grading workflows, enabling students to analyze sensor data and solve numerical physics problems efficiently within a managed, version-controlled environment using GitHub.
May 2026 monthly summary focusing on key accomplishments for the ubsuny/PHY386 project. Delivered an end-to-end Kepler Objects of Interest (KOI) classification system to automate vetting of exoplanet candidates, with emphasis on business value and technical robustness. The work centered on training and evaluating multiple classifiers, data cleaning, feature engineering, and cross-mission generalization to TESS.
May 2026 monthly summary focusing on key accomplishments for the ubsuny/PHY386 project. Delivered an end-to-end Kepler Objects of Interest (KOI) classification system to automate vetting of exoplanet candidates, with emphasis on business value and technical robustness. The work centered on training and evaluating multiple classifiers, data cleaning, feature engineering, and cross-mission generalization to TESS.
April 2026 monthly summary for ubsuny/PHY386. Delivered tangible features and reliability improvements that directly support student learning, reproducible workflows, and data-driven analysis. The work focused on expanding Jupyter-based beam theory tooling, stabilizing physics simulations, enabling stellar classification analyses, and launching a cohesive course project framework with templates and AI-guided guidance.
April 2026 monthly summary for ubsuny/PHY386. Delivered tangible features and reliability improvements that directly support student learning, reproducible workflows, and data-driven analysis. The work focused on expanding Jupyter-based beam theory tooling, stabilizing physics simulations, enabling stellar classification analyses, and launching a cohesive course project framework with templates and AI-guided guidance.
March 2026: Delivered a comprehensive Physics Data Analysis Education Framework and PDE homework materials in the PHY386 repo, improved data integrity, and streamlined repository maintenance. This work provides students with ready-to-use notebooks and data-analysis workflows for sensor data, visualizations, and GitHub-based project management, while ensuring reliable analyses and a cleaner codebase for ongoing development and scalability.
March 2026: Delivered a comprehensive Physics Data Analysis Education Framework and PDE homework materials in the PHY386 repo, improved data integrity, and streamlined repository maintenance. This work provides students with ready-to-use notebooks and data-analysis workflows for sensor data, visualizations, and GitHub-based project management, while ensuring reliable analyses and a cleaner codebase for ongoing development and scalability.
February 2026 monthly summary for repo ubsuny/PHY386: Delivered initial bootstrap and Colab-based scaffolding, establishing a reproducible development environment and a solid project boilerplate to accelerate downstream work. No major bug fixes were recorded this month; focus remained on setting up the foundation for rapid feature development and onboarding.
February 2026 monthly summary for repo ubsuny/PHY386: Delivered initial bootstrap and Colab-based scaffolding, establishing a reproducible development environment and a solid project boilerplate to accelerate downstream work. No major bug fixes were recorded this month; focus remained on setting up the foundation for rapid feature development and onboarding.

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