alan-turing-institute/autoemulate
Mar 2025 – Sep 2025
5 Months active
Languages Used
Jupyter NotebookPythonJSONYAMLpython
Technical Skills
AutoencodersCode RefactoringData ManagementData PreprocessingData ScienceData Visualization
Paolo Conti
PROFILE
Paolo Conti
Paolo Conti contributed to the alan-turing-institute/autoemulate repository by developing and refining machine learning pipelines for scientific emulation, focusing on dimensionality reduction, uncertainty quantification, and physics-based simulation. He implemented robust data preprocessing and model introspection features using Python, NumPy, and PyTorch, enabling scalable workflows for researchers. Paolo introduced new simulators for reaction-diffusion and advection-diffusion processes, integrated VAE and FNO-based emulators, and improved visualization and documentation for reproducibility. His work addressed compatibility and data handling issues, enhanced testing infrastructure, and delivered assets for demonstration purposes, reflecting a deep understanding of scientific computing and a methodical approach to engineering complex ML systems.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
32Total
Bugs
3
Commits
32
Features
9
Lines of code
10,005
Activity Months5
Your Network
38 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Implemented Advection-Diffusion Simulator with FNO Notebook for alan-turing-institute/autoemulate. Delivered a Python-based advection-diffusion solver using NumPy/SciPy, with a companion notebook showing data preparation, training and prediction of an FNO emulator, and evaluation via R2 score; lays groundwork for PyTorch integration and a reproducible benchmarking workflow.
1 Commits • 1 Features
Sep 1, 2025September 2025: Implemented Advection-Diffusion Simulator with FNO Notebook for alan-turing-institute/autoemulate. Delivered a Python-based advection-diffusion solver using NumPy/SciPy, with a companion notebook showing data preparation, training and prediction of an FNO emulator, and evaluation via R2 score; lays groundwork for PyTorch integration and a reproducible benchmarking workflow.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for alan-turing-institute/autoemulate: Delivered a new Reaction-Diffusion GIF Asset to the misc directory for demos, UI previews, and demonstration content. This asset enhances demo readiness and internal testing, with clear traceability via a committed change. No major bugs were fixed this month. Overall impact: accelerates client demos and UI previews, improves asset management, and demonstrates commitment to maintaining demonstrable content for showcases.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for alan-turing-institute/autoemulate: Delivered a new Reaction-Diffusion GIF Asset to the misc directory for demos, UI previews, and demonstration content. This asset enhances demo readiness and internal testing, with clear traceability via a committed change. No major bugs were fixed this month. Overall impact: accelerates client demos and UI previews, improves asset management, and demonstrates commitment to maintaining demonstrable content for showcases.
July 2025
1 Commits
Jul 1, 2025July 2025 monthly work summary for alan-turing-institute/autoemulate focusing on bug fixes and data handling improvements in the experimental pipeline.
1 Commits
Jul 1, 2025July 2025 monthly work summary for alan-turing-institute/autoemulate focusing on bug fixes and data handling improvements in the experimental pipeline.
July 2025
April 2025
12 Commits • 4 Features
Apr 1, 2025April 2025 monthly summary for alan-turing-institute/autoemulate: Implemented robust AutoEmulatePipeline preprocessing and feature improvements, fixed critical compatibility issues in the output pipeline and hyperparameter search, hardened testing infrastructure, and enhanced tutorial visuals and documentation. Resulting in more reliable experimentation, faster iteration, and clearer onboarding for new users.
April 2025
12 Commits • 4 Features
Apr 1, 2025April 2025 monthly summary for alan-turing-institute/autoemulate: Implemented robust AutoEmulatePipeline preprocessing and feature improvements, fixed critical compatibility issues in the output pipeline and hyperparameter search, hardened testing infrastructure, and enhanced tutorial visuals and documentation. Resulting in more reliable experimentation, faster iteration, and clearer onboarding for new users.
March 2025
17 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for alan-turing-institute/autoemulate: Delivered major platform enhancements enabling scalable dimensionality reduction workflows, robust model introspection, and uncertainty-aware simulations. Key improvements include: (1) AutoEmulate Dimensionality Reduction and Preprocessing Enhancements: dimensionality reduction factory, output scaling, support for multiple reducers, pretrained reducers, a non-trainable Reducer wrapper, and tighter pipeline integration for improved preprocessing and model comparison. (2) Model introspection enhancements: added TransformedTargetRegressor support and unwrapped nested target transformers for robust model identification. (3) Reaction-Diffusion Modeling and reduced-dimension emulators: introduced a reaction-diffusion simulator, VAE-based output reduction, tutorials, enhanced visualizations, and uncertainty-aware predictions (including predict_with_std). (4) Uncertainty Quantification Visualization bug fix: improved reconstruction and reliability of UQ visuals. These changes collectively accelerate experimentation, improve model reliability, and broaden the practical utility of AutoEmulate for researchers and data-driven decision-making.
17 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for alan-turing-institute/autoemulate: Delivered major platform enhancements enabling scalable dimensionality reduction workflows, robust model introspection, and uncertainty-aware simulations. Key improvements include: (1) AutoEmulate Dimensionality Reduction and Preprocessing Enhancements: dimensionality reduction factory, output scaling, support for multiple reducers, pretrained reducers, a non-trainable Reducer wrapper, and tighter pipeline integration for improved preprocessing and model comparison. (2) Model introspection enhancements: added TransformedTargetRegressor support and unwrapped nested target transformers for robust model identification. (3) Reaction-Diffusion Modeling and reduced-dimension emulators: introduced a reaction-diffusion simulator, VAE-based output reduction, tutorials, enhanced visualizations, and uncertainty-aware predictions (including predict_with_std). (4) Uncertainty Quantification Visualization bug fix: improved reconstruction and reliability of UQ visuals. These changes collectively accelerate experimentation, improve model reliability, and broaden the practical utility of AutoEmulate for researchers and data-driven decision-making.
March 2025
Activity
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Quality Metrics
Correctness84.4%
Maintainability83.4%
Architecture79.4%
Performance70.6%
AI Usage22.6%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPythonYAMLpython
Technical Skills
AutoencodersCode RefactoringData HandlingData ManagementData PreprocessingData ScienceData VisualizationDebuggingDeep LearningDimensionality ReductionDocumentationEmulator DevelopmentEnvironment ManagementExperimental SetupFeature Scaling
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