m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPythonText
Technical Skills
Data ScienceDeep LearningJupyter NotebookMachine LearningModel ExplanationNeural Operator
Alessandro Trenta
PROFILE
Alessandro Trenta
Alessandro Trenta developed a robust Fourier Neural Operator pipeline for the Kuramoto-Sivashinsky equation as part of the m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon repository. He implemented end-to-end data loading, model definition, training, and prediction workflows using Python, PyTorch, and Jupyter Notebook, focusing on scientific computing and deep learning best practices. Alessandro addressed critical output shape issues in ks_prediction.npy, ensuring consistent results and reproducibility across teams. His work included comprehensive notebook cleanup and documentation, improving code readability and maintainability. The depth of his contributions enabled faster collaboration and clearer model explanations, supporting both technical accuracy and cross-team alignment.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
1
Lines of code
1,842
Activity Months1
Your Network
7 people
Work History
April 2025
7 Commits • 1 Features
Apr 1, 2025April 2025 contributions focused on expanding the ML4DE hackathon project with a Fourier Neural Operator (FNO) for the Kuramoto-Sivashinsky equation, alongside critical data and output stabilization work. Delivered a robust FNO pipeline (data loading, model definition, training, and prediction) and completed comprehensive notebook cleanup and documentation. Fixed key output issues and aligned ks_prediction outputs across teams to ensure consistent results, enabling faster collaboration and reproducibility.
7 Commits • 1 Features
Apr 1, 2025April 2025 contributions focused on expanding the ML4DE hackathon project with a Fourier Neural Operator (FNO) for the Kuramoto-Sivashinsky equation, alongside critical data and output stabilization work. Delivered a robust FNO pipeline (data loading, model definition, training, and prediction) and completed comprehensive notebook cleanup and documentation. Fixed key output issues and aligned ks_prediction outputs across teams to ensure consistent results, enabling faster collaboration and reproducibility.
April 2025
Activity
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Quality Metrics
Correctness68.6%
Maintainability74.4%
Architecture65.6%
Performance65.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonText
Technical Skills
Data ScienceDeep LearningJupyter NotebookMachine LearningModel ExplanationNeural OperatorNumPyPartial Differential EquationsPyTorchPythonScientific ComputingScikit-learn
Repositories Contributed To
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