arvindkrishna87/STAT390_SP25_CMIL
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Computer VisionData ScienceDeep LearningJupyter NotebooksMachine LearningModel Evaluation
Josiah (Jojo) Kim
PROFILE
Josiah (jojo) Kim
Arvind Krishna developed and evaluated adaptive pooling window sizes within a ResNet model enhanced with CBAM and class balancing for the arvindkrishna87/STAT390_SP25_CMIL repository. Using Python and Jupyter Notebooks, he implemented multiple pooling strategies, designed experiments, and updated artifacts to ensure reproducibility and traceability. His work focused on identifying the most effective pooling configuration to improve model performance on imbalanced datasets, providing a data-driven foundation for deployment decisions. The approach demonstrated depth in computer vision and model evaluation, with careful attention to experimental rigor and workflow. No bugs were reported or fixed, reflecting a focused and methodical engineering effort.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
11,050
Activity Months1
Your Network
8 people
Work History
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for arvindkrishna87/STAT390_SP25_CMIL: Focused feature work delivering an evidence-based evaluation of adaptive pooling window sizes in a ResNet model augmented with CBAM and class balancing. The work included implementation, experimentation, and artifact updates to enable reproducible assessments, setting the stage for data-driven deployment decisions and potential performance gains on imbalanced datasets. No major bugs reported or fixed this month. This effort demonstrates strong capabilities in CNN architectures, experimentation workflow, and rigorous validation.
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for arvindkrishna87/STAT390_SP25_CMIL: Focused feature work delivering an evidence-based evaluation of adaptive pooling window sizes in a ResNet model augmented with CBAM and class balancing. The work included implementation, experimentation, and artifact updates to enable reproducible assessments, setting the stage for data-driven deployment decisions and potential performance gains on imbalanced datasets. No major bugs reported or fixed this month. This effort demonstrates strong capabilities in CNN architectures, experimentation workflow, and rigorous validation.
May 2025
Activity
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Quality Metrics
Correctness73.4%
Maintainability73.4%
Architecture66.6%
Performance66.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Computer VisionData ScienceDeep LearningJupyter NotebooksMachine LearningModel EvaluationPython
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