Fernando-JAL/Neurociencias-2025-2
Apr 2025 – May 2025
2 Months active
Languages Used
Python
Technical Skills
ClusteringData AnalysisData VisualizationGaussian Mixture ModelsJupyter NotebookK-Means
Ferlo-17
PROFILE
Ferlo-17
During two months contributing to Fernando-JAL/Neurociencias-2025-2, Fernanda López Arguelles developed three features focused on clustering analysis and deep learning for neuroscience data. She implemented an end-to-end clustering workflow on coactivation matrices, using Python and Scikit-learn to apply the elbow method, K-Means, and Gaussian Mixture Models, with PCA-based visualizations for method comparison. In May, she created student-facing exam resources and deep learning Jupyter notebooks for brain image analysis, including CNN-based tumor classification and image preprocessing with Keras and TensorFlow. Her work demonstrated depth in data preprocessing, reproducibility, and research enablement, though no bug fixes were recorded.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
11,303
Activity Months2
Your Network
37 people
Work History
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for Fernando-JAL/Neurociencias-2025-2: Delivered new student-facing exam resource and two neuroscience deep-learning notebooks, strengthening teaching materials and research capabilities. These artifacts enhance accessibility, reproducibility, and experimentation in brain-image analysis tasks, enabling faster student prep and prototype development for brain tumor classification.
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for Fernando-JAL/Neurociencias-2025-2: Delivered new student-facing exam resource and two neuroscience deep-learning notebooks, strengthening teaching materials and research capabilities. These artifacts enhance accessibility, reproducibility, and experimentation in brain-image analysis tasks, enabling faster student prep and prototype development for brain tumor classification.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025Delivered the Coactivation Matrix Clustering Analysis feature in April 2025 for Fernando-JAL/Neurociencias-2025-2. Implemented an end-to-end clustering workflow on the Coactivation_matrix dataset: elbow method for optimal cluster count, K-Means and Gaussian Mixture Models on scaled data, with PCA-based visualizations to compare approaches. This enables data-driven interpretation of coactivation patterns, supports method benchmarking, and informs experimental prioritization. No major bugs fixed this month; focus was on feature delivery and validation.
April 2025
2 Commits • 1 Features
Apr 1, 2025Delivered the Coactivation Matrix Clustering Analysis feature in April 2025 for Fernando-JAL/Neurociencias-2025-2. Implemented an end-to-end clustering workflow on the Coactivation_matrix dataset: elbow method for optimal cluster count, K-Means and Gaussian Mixture Models on scaled data, with PCA-based visualizations to compare approaches. This enables data-driven interpretation of coactivation patterns, supports method benchmarking, and informs experimental prioritization. No major bugs fixed this month; focus was on feature delivery and validation.
Activity
Loading activity data...
Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
ClusteringData AnalysisData PreprocessingData ScienceData VisualizationDeep LearningExploratory Data AnalysisGaussian Mixture ModelsImage ClassificationImage PreprocessingJupyter NotebookK-MeansKerasMachine LearningPCA
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline