arvindkrishna87/STAT390_WI2025
Feb 2025 – Mar 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPythonJSON
Technical Skills
Code OrganizationCode RefactoringComputer VisionContour DetectionData AnalysisDocumentation
Hannah Ma
PROFILE
Hannah Ma
Developed and maintained a contour-based tissue patch extraction and analysis pipeline for the arvindkrishna87/STAT390_WI2025 repository, focusing on automating patch workflows and improving data integrity. Leveraged Python, OpenCV, and Pandas to implement nested contour detection, grayscale inversion for thresholding, and patch alignment with overlap removal. Enhanced project structure by reorganizing assets, expanding histology image datasets, and updating documentation to support reproducibility and demonstrations. Introduced a Jupyter-based toolkit for automated patching, including mask reading, contour detection, overlap computation, and export formatting. Addressed repository hygiene by removing obsolete notebooks and adding scripts for data cleaning, supporting collaborative and repeatable research.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
4
Lines of code
6,940
Activity Months2
Your Network
126 peopleSame Organization
@u.northwestern.edu114
Work History
March 2025
4 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03 for arvindkrishna87/STAT390_WI2025: Focused on delivering end-to-end patch automation, tooling, and project hygiene to accelerate research workflows and improve data integrity. Key work spanned three main areas: (1) Automated Patch Notebook: introduced a Jupyter-based toolkit for automated code patching and export, including functions to read masks, detect contours, compute patch overlaps, export patched slices, and metrics to evaluate patch coverage and performance; plus enhancements to the patch export output. (2) Patch management tooling and documentation: updated README files for Presentations 5 and 6, added a Python script to filter erroneous uploads in the Google Drive “Patches” folder, adjusted image resolution for patch overview visuals, and provided Colab/QUEST research context. (3) Bug fix and repo hygiene: removed an obsolete Presentation 5 notebook to prevent confusion and ensure alignment with current work. Overall impact: increased automation and reliability of patch workflows, improved data quality for evaluation, and clearer documentation to support collaboration and reproducibility. Technologies/skills demonstrated: Python scripting, Jupyter notebooks, image processing (masks, contours), patch overlap calculations, export formatting, Colab/QUEST context, Google Drive automation, and documentation practices. Business value: reduces manual patching effort, minimizes incorrect uploads, enhances repeatability of experiments, and accelerates research iteration.
4 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03 for arvindkrishna87/STAT390_WI2025: Focused on delivering end-to-end patch automation, tooling, and project hygiene to accelerate research workflows and improve data integrity. Key work spanned three main areas: (1) Automated Patch Notebook: introduced a Jupyter-based toolkit for automated code patching and export, including functions to read masks, detect contours, compute patch overlaps, export patched slices, and metrics to evaluate patch coverage and performance; plus enhancements to the patch export output. (2) Patch management tooling and documentation: updated README files for Presentations 5 and 6, added a Python script to filter erroneous uploads in the Google Drive “Patches” folder, adjusted image resolution for patch overview visuals, and provided Colab/QUEST research context. (3) Bug fix and repo hygiene: removed an obsolete Presentation 5 notebook to prevent confusion and ensure alignment with current work. Overall impact: increased automation and reliability of patch workflows, improved data quality for evaluation, and clearer documentation to support collaboration and reproducibility. Technologies/skills demonstrated: Python scripting, Jupyter notebooks, image processing (masks, contours), patch overlap calculations, export formatting, Colab/QUEST context, Google Drive automation, and documentation practices. Business value: reduces manual patching effort, minimizes incorrect uploads, enhances repeatability of experiments, and accelerates research iteration.
March 2025
February 2025
8 Commits • 2 Features
Feb 1, 2025February 2025 — arvindkrishna87/STAT390_WI2025: Delivered key enhancements in contour-based tissue patch extraction and analysis, and improved repository assets and documentation for reproducibility and demos.
February 2025
8 Commits • 2 Features
Feb 1, 2025February 2025 — arvindkrishna87/STAT390_WI2025: Delivered key enhancements in contour-based tissue patch extraction and analysis, and improved repository assets and documentation for reproducibility and demos.
Activity
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Quality Metrics
Correctness83.4%
Maintainability85.0%
Architecture83.4%
Performance75.0%
AI Usage23.4%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
Code OrganizationCode RefactoringComputer VisionContour DetectionData AnalysisData CleaningData VisualizationDocumentationFile ManagementImage ProcessingJupyter NotebooksMatplotlibNumPyOpenCVPandas
Repositories Contributed To
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