Over four months, contributed to the Purdue-Artificial-Intelligence-in-Music/Evaluator-code repository by building a cross-platform mobile application focused on real-time video analysis and computer vision. Developed features such as camera capture, live frame streaming via WebSockets, and a native video analysis module with platform-specific implementations in JavaScript, TypeScript, and C++. Integrated OpenCV and FFmpeg for advanced video processing, while enhancing the user experience with authentication flows and responsive UI overlays. Emphasized maintainable code through systematic refactoring and dead code removal, enabling scalable media analytics and stable evaluation pipelines for AI-in-music research without introducing regressions or unresolved bugs.
October 2025 performance summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code focused on delivering a robust, user-friendly video evaluation pipeline and secure access controls. Key work centered on video analysis enhancements with camera integration, and a streamlined user authentication flow to support research collaboration and demos.
October 2025 performance summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code focused on delivering a robust, user-friendly video evaluation pipeline and secure access controls. Key work centered on video analysis enhancements with camera integration, and a streamlined user authentication flow to support research collaboration and demos.
September 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code: Focused on delivering end-to-end CV and media-processing improvements, performance optimizations, and code hygiene, delivering business value through enhanced capabilities and maintainability.
September 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code: Focused on delivering end-to-end CV and media-processing improvements, performance optimizations, and code hygiene, delivering business value through enhanced capabilities and maintainability.
August 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code. Delivered a native video analysis module with platform-specific implementations (Android and iOS), TypeScript definitions, and integration into the main application. The module enables analyzing video properties such as duration and dimensions, establishing a scalable foundation for cross-platform media analytics and future feature development.
August 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code. Delivered a native video analysis module with platform-specific implementations (Android and iOS), TypeScript definitions, and integration into the main application. The module enables analyzing video properties such as duration and dimensions, establishing a scalable foundation for cross-platform media analytics and future feature development.
In July 2025, delivered foundational features and real-time capabilities for the Evaluator-code repository, establishing a solid base for ongoing AI-in-Music experiments and scalable data capture. Key outcomes include mobile camera capture with backend integration, real-time frame streaming via WebSockets, robust video upload flow, and enhanced point/line processing. These workstreams enabled faster data collection, improved data reliability, and live analytics feedback for iteration and validation.
In July 2025, delivered foundational features and real-time capabilities for the Evaluator-code repository, establishing a solid base for ongoing AI-in-Music experiments and scalable data capture. Key outcomes include mobile camera capture with backend integration, real-time frame streaming via WebSockets, robust video upload flow, and enhanced point/line processing. These workstreams enabled faster data collection, improved data reliability, and live analytics feedback for iteration and validation.
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