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
In April 2025, Alessandro Trenta contributed to the m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon repository by developing a Fourier Neural Operator pipeline for the Kuramoto-Sivashinsky equation. He implemented data loading, model definition, training, and prediction using Python, PyTorch, and NumPy within Jupyter Notebooks. Alessandro addressed output stabilization by fixing the ks_prediction.npy shape, ensuring consistent results across team workflows. He also refactored and cleaned the Kuramoto-Sivashinsky workflow, improving code readability and maintainability. His work included comprehensive notebook documentation and clear model explanations, supporting reproducibility and collaboration. The contributions demonstrated depth in scientific computing and machine learning model integration.
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 peopleShared Repositories
7
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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