natmourajr/CPE883-2025-02
Jul 2025 – Aug 2025
2 Months active
Languages Used
BashCSVGitJupyter NotebookMarkdownPythonSQLShell
Technical Skills
BenchmarkingCode OrganizationData AnalysisData DocumentationData EngineeringData Loading
Felipe
PROFILE
Felipe
Felipe Munaro Lima developed scalable, reproducible time series modeling pipelines in the natmourajr/CPE883-2025-02 repository over two months. He implemented an end-to-end data ingestion and baseline training workflow, introducing a modular main.py and a Collector class for benchmark data loading. Leveraging Python, PyTorch, and Docker, Felipe unified architectures such as Capsule Networks, CNNs, LSTM, and TKAN, and integrated advanced frequency-domain transformations using CEEMDAN. His work included project refactoring for maintainability, submodule management, and improved configuration handling. These efforts established a robust foundation for rapid experimentation, clearer onboarding, and future production deployment, reflecting strong depth in data engineering and deep learning.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
21Total
Bugs
0
Commits
21
Features
5
Lines of code
137,139
Activity Months2
Your Network
14 people
Work History
August 2025
5 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: This month focused on delivering foundational capabilities for time-series modeling and data processing, with an emphasis on reusable architectures and scalable pipelines. No major bugs fixed were documented in this period; efforts were concentrated on feature delivery and project refactor to enable faster experimentation and improved maintainability. Key outcomes include a unified Time Series Modeling Framework and a CEEMDAN data processing pipeline, along with project structure improvements that clarify ownership and future extension. Overall, these developments increase business value by enabling rapid prototyping across models, supporting advanced frequency-domain transformations, and providing a clearer, more maintainable codebase for future work.
5 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: This month focused on delivering foundational capabilities for time-series modeling and data processing, with an emphasis on reusable architectures and scalable pipelines. No major bugs fixed were documented in this period; efforts were concentrated on feature delivery and project refactor to enable faster experimentation and improved maintainability. Key outcomes include a unified Time Series Modeling Framework and a CEEMDAN data processing pipeline, along with project structure improvements that clarify ownership and future extension. Overall, these developments increase business value by enabling rapid prototyping across models, supporting advanced frequency-domain transformations, and providing a clearer, more maintainable codebase for future work.
August 2025
July 2025
16 Commits • 3 Features
Jul 1, 2025July 2025: Implemented an end-to-end data ingestion and baseline training pipeline, enabling reproducible training runs via a new main.py, a Collector for benchmark data loading, improved data loading paths and configuration management, and cleanup of tracked data directories. Added time-series forecasting experiments using KAN Transformer and KAT, including 1D adaptations, synthetic data utilities, and integration of the 3W dataset with classification capabilities. Reorganized the repository for TKAT experiments, converting rational_kat_cu into a submodule, updating submodule pointers, and documenting workflows. Improved repository hygiene by removing data from version control, stabilizing submodules, and cleaning notebooks. Overall, these efforts deliver a scalable, reproducible research workflow with a clearer onboarding path and a foundation ready for production deployment.
July 2025
16 Commits • 3 Features
Jul 1, 2025July 2025: Implemented an end-to-end data ingestion and baseline training pipeline, enabling reproducible training runs via a new main.py, a Collector for benchmark data loading, improved data loading paths and configuration management, and cleanup of tracked data directories. Added time-series forecasting experiments using KAN Transformer and KAT, including 1D adaptations, synthetic data utilities, and integration of the 3W dataset with classification capabilities. Reorganized the repository for TKAT experiments, converting rational_kat_cu into a submodule, updating submodule pointers, and documenting workflows. Improved repository hygiene by removing data from version control, stabilizing submodules, and cleaning notebooks. Overall, these efforts deliver a scalable, reproducible research workflow with a clearer onboarding path and a foundation ready for production deployment.
Activity
Loading activity data...
Quality Metrics
Correctness84.8%
Maintainability84.8%
Architecture84.8%
Performance73.4%
AI Usage22.8%
Skills & Technologies
Programming Languages
BashCSVDockerfileGitJupyter NotebookMarkdownPythonSQLShellTOML
Technical Skills
BenchmarkingCapsule Networks (CapsNet)Code OrganizationConvolutional Neural Networks (CNN)Data AnalysisData DocumentationData EngineeringData LoadingData PreprocessingData ScienceData VisualizationDebuggingDeep LearningDockerEnvironment Variables
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline