Hack4Impact-UMD/food-for-all-dc
Oct 2024 – Apr 2025
5 Months active
Languages Used
CSSJavaScriptTypeScriptHTMLPython
Technical Skills
CSS StylingComponent RefactoringData ManagementFrontend DevelopmentMaterial UIMaterial-UI
tencups
PROFILE
Tencups
Michael Wang contributed to Hack4Impact-UMD/food-for-all-dc by engineering robust data management and delivery routing features that streamlined client operations. He unified spreadsheet-based CRUD workflows, integrated Firestore and Firebase for real-time data persistence, and refactored frontend components using React and TypeScript to improve maintainability. On the backend, Michael implemented clustering algorithms in Python to optimize delivery routes, leveraging geospatial analysis and Haversine distance calculations. He enhanced operational visibility with map-based visualizations and automated CSV exports, including ZIP archiving and email distribution via Google Cloud Functions. His work reduced manual overhead, improved data accuracy, and established scalable, testable pipelines for ongoing enhancements.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
19Total
Bugs
2
Commits
19
Features
10
Lines of code
15,353
Activity Months5
Your Network
15 people
Work History
April 2025
6 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Hack4Impact-UMD/food-for-all-dc. Focused on delivering a robust data export pipeline and proactive delivery communications, with measurable improvements to ops analytics and reporting reliability.
6 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Hack4Impact-UMD/food-for-all-dc. Focused on delivering a robust data export pipeline and proactive delivery communications, with measurable improvements to ops analytics and reporting reliability.
April 2025
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 performance focused on delivering value through improved delivery routing workflows, robust clustering stability, and scalable route distribution. Implemented proximity-based addition of delivery locations to existing clusters with dynamic HTML visualizations to compare before/after cluster states, enabling better route planning and faster decision-making. Hardened clustering to handle mismatches between driver count and deliveries, reducing fragmentation and surfacing actionable errors when drivers exceed deliveries. Delivered per-cluster CSV export and distribution to drivers, including a local test page to validate CSV generation, and support for emailing route files to drivers. These workstreams collectively reduce manual routing overhead, improve driver utilization, and enable data-driven planning.
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 performance focused on delivering value through improved delivery routing workflows, robust clustering stability, and scalable route distribution. Implemented proximity-based addition of delivery locations to existing clusters with dynamic HTML visualizations to compare before/after cluster states, enabling better route planning and faster decision-making. Hardened clustering to handle mismatches between driver count and deliveries, reducing fragmentation and surfacing actionable errors when drivers exceed deliveries. Delivered per-cluster CSV export and distribution to drivers, including a local test page to validate CSV generation, and support for emailing route files to drivers. These workstreams collectively reduce manual routing overhead, improve driver utilization, and enable data-driven planning.
February 2025
3 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for Hack4Impact-UMD/food-for-all-dc focused on clustering reliability, backend readiness, and operational visibility. Key deliverables improved geographic clustering accuracy, persisted data support, and visualization for delivery operations, while strengthening test coverage and edge-case robustness. Key features delivered: - Clustering improvements and testing enhancements: explicit test_clustering for K-Means and K-Medoids; refactored distance calculation to use the Haversine formula; improved handling of edge cases in cluster size constraints. (Commits: 07c6fb7fced5b31ceaae0c9dc80ad63779e86738) - Firebase backend integration: Python Firebase connection, database operations module, Admin SDK setup; updated .gitignore; refactored clustering tests to align with backend. (Commits: dbb9a569ae3220d7afdc91daaf8811e926b8d0cb) - Delivery clusters visualization on map: added display_clusters_on_map visualization, HTML rendering, and testing utilities to validate clustering and map rendering. (Commits: b8139baf12b02bb3e086923a0f917b9c62ff4471) Major bugs fixed / robustness improvements: - Addressed edge-case handling in clustering size constraints and improved distance-based calculations for more stable clustering results. Aligned tests with backend setup to reduce flaky failures. Overall impact and accomplishments: - Achieved more accurate, reliable delivery clustering and better operational visibility through map-based visualization. Backend readiness enables scalable data persistence and real-time extensions. Improved test coverage reduces regression risk and accelerates future work. Technologies/skills demonstrated: - Python, K-Means / K-Medoids clustering, Haversine distance, test-driven development, Firebase Admin SDK integration, Python backend modules, HTML visualization, and repository hygiene.
3 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for Hack4Impact-UMD/food-for-all-dc focused on clustering reliability, backend readiness, and operational visibility. Key deliverables improved geographic clustering accuracy, persisted data support, and visualization for delivery operations, while strengthening test coverage and edge-case robustness. Key features delivered: - Clustering improvements and testing enhancements: explicit test_clustering for K-Means and K-Medoids; refactored distance calculation to use the Haversine formula; improved handling of edge cases in cluster size constraints. (Commits: 07c6fb7fced5b31ceaae0c9dc80ad63779e86738) - Firebase backend integration: Python Firebase connection, database operations module, Admin SDK setup; updated .gitignore; refactored clustering tests to align with backend. (Commits: dbb9a569ae3220d7afdc91daaf8811e926b8d0cb) - Delivery clusters visualization on map: added display_clusters_on_map visualization, HTML rendering, and testing utilities to validate clustering and map rendering. (Commits: b8139baf12b02bb3e086923a0f917b9c62ff4471) Major bugs fixed / robustness improvements: - Addressed edge-case handling in clustering size constraints and improved distance-based calculations for more stable clustering results. Aligned tests with backend setup to reduce flaky failures. Overall impact and accomplishments: - Achieved more accurate, reliable delivery clustering and better operational visibility through map-based visualization. Backend readiness enables scalable data persistence and real-time extensions. Improved test coverage reduces regression risk and accelerates future work. Technologies/skills demonstrated: - Python, K-Means / K-Medoids clustering, Haversine distance, test-driven development, Firebase Admin SDK integration, Python backend modules, HTML visualization, and repository hygiene.
February 2025
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for Hack4Impact-UMD/food-for-all-dc focused on data management improvements, UI simplification, and reliable calendar operations. Implemented Firestore-backed client data management, enhanced delivery creation UX with dynamic dropdowns, and stabilized calendar filtering. These changes increased data accuracy, reduced manual entry, and improved operational reliability for delivery planning and client data governance.
November 2024
4 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for Hack4Impact-UMD/food-for-all-dc focused on data management improvements, UI simplification, and reliable calendar operations. Implemented Firestore-backed client data management, enhanced delivery creation UX with dynamic dropdowns, and stabilized calendar filtering. These changes increased data accuracy, reduced manual entry, and improved operational reliability for delivery planning and client data governance.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for Hack4Impact-UMD/food-for-all-dc: Delivered a streamlined spreadsheet-based client data management experience that unifies CRUD operations and improves data governance, introduced quick-edit/delete actions via a three-dots menu, and enhanced usability by removing legacy UserProfile component. Implemented robust filtering (by zip code, last name, dietary restrictions) and sorting by Name (lastname). Fixed styling regressions to ensure a consistent UI. The updates reduce manual data-entry time, minimize data-entry errors, and provide scalable, maintainable data workflows across the spreadsheet view. These changes lay groundwork for broader data-management enhancements across the platform.
2 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for Hack4Impact-UMD/food-for-all-dc: Delivered a streamlined spreadsheet-based client data management experience that unifies CRUD operations and improves data governance, introduced quick-edit/delete actions via a three-dots menu, and enhanced usability by removing legacy UserProfile component. Implemented robust filtering (by zip code, last name, dietary restrictions) and sorting by Name (lastname). Fixed styling regressions to ensure a consistent UI. The updates reduce manual data-entry time, minimize data-entry errors, and provide scalable, maintainable data workflows across the spreadsheet view. These changes lay groundwork for broader data-management enhancements across the platform.
October 2024
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Quality Metrics
Correctness82.2%
Maintainability81.0%
Architecture78.4%
Performance74.8%
AI Usage23.2%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptPythonTypeScript
Technical Skills
API IntegrationBackend DevelopmentCSS StylingCSV GenerationCloud FunctionsCloud ServicesClustering AlgorithmsComponent RefactoringData ExportData ManagementData ScienceData VisualizationDatabase ManagementEmail IntegrationError Handling
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