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
Contributed to the m4DL-Mathematics-for-Deep-Learning/ML4DE_hackathon repository by developing a Fourier Neural Operator pipeline to solve the Kuramoto-Sivashinsky equation, focusing on data loading, model definition, training, and prediction within Jupyter Notebooks. Addressed a critical bug by correcting the shape of ks_prediction.npy outputs, ensuring consistency and reproducibility across team workflows. Enhanced the project’s maintainability by refactoring code, cleaning up notebooks, and improving documentation for clearer model explanations. Leveraged Python, PyTorch, and NumPy to implement deep learning solutions for scientific computing challenges, enabling more robust collaboration and streamlined experimentation for machine learning applications involving partial differential equations.
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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