arvindkrishna87/STAT390_SP25_CMIL
Apr 2025 – Jun 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Computer VisionData AnalysisData ProcessingFile ManagementImage AnalysisImage Processing
Akhil Kommala
PROFILE
Akhil Kommala
Akhil Kommala developed and delivered a suite of machine learning and computer vision features for the arvindkrishna87/STAT390_SP25_CMIL repository over three months. He built Jupyter Notebook workflows for image slice renaming, mask processing, and decision method evaluation, emphasizing robust path handling and reproducible testing. Using Python and PyTorch, Akhil consolidated preprocessing steps, integrated Otsu thresholding for improved mask generation, and established end-to-end pipelines for CoatNet model deployment. His work included stakeholder-facing documentation and presentation assets, supporting collaboration and traceability. The engineering demonstrated depth in code organization, data management, and model testing, resulting in production-ready, maintainable solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
9
Lines of code
268,851
Activity Months3
Your Network
8 people
Work History
June 2025
5 Commits • 5 Features
Jun 1, 2025June 2025 monthly summary focusing on CoatNet artifacts delivered for production readiness in arvindkrishna87/STAT390_SP25_CMIL. The work concentrated on uploading CoatNet code and models, along with stain ensemble code, to the repository and to the storage/deployment environment, establishing a traceable, end-to-end path for inference. These deliveries improve reproducibility, accelerate future model updates, and prepare the project for production rollouts. No major bugs were reported or fixed this month.
5 Commits • 5 Features
Jun 1, 2025June 2025 monthly summary focusing on CoatNet artifacts delivered for production readiness in arvindkrishna87/STAT390_SP25_CMIL. The work concentrated on uploading CoatNet code and models, along with stain ensemble code, to the repository and to the storage/deployment environment, establishing a traceable, end-to-end path for inference. These deliveries improve reproducibility, accelerate future model updates, and prepare the project for production rollouts. No major bugs were reported or fixed this month.
June 2025
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 monthly summary for arvindkrishna87/STAT390_SP25_CMIL. Key features and artifacts delivered include stakeholder-facing resources and a new decision-method evaluation workflow, both aimed at accelerating collaboration, dissemination, and reliable method validation. Delivered stakeholder documents and presentation materials across Presentation directories, including PDFs such as Epithelium Questions, Patching Team Presentation, Case-Level Decision Method Ideas, Case-Level Decision Method Testing, and STAT 390 Presentation 5. Introduced a Decision Method Evaluation Notebook and testing framework to enable reproducible evaluation of the decision method across stains and cases. Impact: improved cross-team communication, faster stakeholder alignment, and a scalable testing approach that supports ongoing method refinement. Technologies/skills demonstrated: Python, Jupyter notebooks, data science testing, document generation, and robust Git-based version control with clear, descriptive commit messages.
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 monthly summary for arvindkrishna87/STAT390_SP25_CMIL. Key features and artifacts delivered include stakeholder-facing resources and a new decision-method evaluation workflow, both aimed at accelerating collaboration, dissemination, and reliable method validation. Delivered stakeholder documents and presentation materials across Presentation directories, including PDFs such as Epithelium Questions, Patching Team Presentation, Case-Level Decision Method Ideas, Case-Level Decision Method Testing, and STAT 390 Presentation 5. Introduced a Decision Method Evaluation Notebook and testing framework to enable reproducible evaluation of the decision method across stains and cases. Impact: improved cross-team communication, faster stakeholder alignment, and a scalable testing approach that supports ongoing method refinement. Technologies/skills demonstrated: Python, Jupyter notebooks, data science testing, document generation, and robust Git-based version control with clear, descriptive commit messages.
April 2025
4 Commits • 2 Features
Apr 1, 2025Monthly summary for 2025-04 highlighting delivery of a cohesive notebook-based feature for image slice renaming and mask processing, plus a team presentation asset. Focused on reliability and data quality through robust path handling (dirpath) and Otsu thresholding, consolidating initial implementations into a single, user-facing workflow. Also delivered supporting presentation material to facilitate team communication.
4 Commits • 2 Features
Apr 1, 2025Monthly summary for 2025-04 highlighting delivery of a cohesive notebook-based feature for image slice renaming and mask processing, plus a team presentation asset. Focused on reliability and data quality through robust path handling (dirpath) and Otsu thresholding, consolidating initial implementations into a single, user-facing workflow. Also delivered supporting presentation material to facilitate team communication.
April 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture87.4%
Performance83.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Code OrganizationComputer VisionData AnalysisData ManagementData ProcessingData VisualizationDeep LearningFile ManagementImage AnalysisImage ProcessingJupyter NotebookMachine LearningModel TestingPyTorchPython
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