alan-turing-institute/autoemulate
Oct 2024 – Feb 2025
5 Months active
Languages Used
PythonSQLpythonyamlMarkdownRSTShellTOML
Technical Skills
CI/CDCode RefactoringConfiguration ManagementData AnalysisData VisualizationDataFrames
mastoffel
PROFILE
Mastoffel
Martin Stoffel contributed to the alan-turing-institute/autoemulate repository, delivering end-to-end improvements across model evaluation, sensitivity analysis, and user onboarding. He enhanced the codebase by refactoring core modules, expanding plotting capabilities, and automating problem inference, which streamlined workflows and improved reproducibility. Using Python, PyTorch, and GitHub Actions, Martin strengthened CI/CD pipelines, stabilized dependencies, and introduced features like terminal-visible plots and model extraction utilities. His work included updating documentation, refining contributor guidelines, and resolving installation and memory handling issues. These efforts resulted in a more robust, maintainable, and user-friendly machine learning toolkit, supporting both development and production environments.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
90Total
Bugs
10
Commits
90
Features
24
Lines of code
3,659
Activity Months5
Your Network
17 people
Work History
February 2025
28 Commits • 12 Features
Feb 1, 2025February 2025 was marked by a set of high-impact, production-ready improvements across back-end, packaging, UI, and visualization for the autoemulate project. The work delivered strong business value by enabling easier distribution, clearer model handling, and richer user interactions while strengthening the codebase and test coverage.
28 Commits • 12 Features
Feb 1, 2025February 2025 was marked by a set of high-impact, production-ready improvements across back-end, packaging, UI, and visualization for the autoemulate project. The work delivered strong business value by enabling easier distribution, clearer model handling, and richer user interactions while strengthening the codebase and test coverage.
February 2025
January 2025
18 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for alan-turing-institute/autoemulate: Delivered customer-facing documentation improvements, strengthened testing infrastructure, resolved a memory-sharing bug, and stabilized dependencies/CI to improve reproducibility and reliability. These efforts reduce onboarding time, increase release confidence, and enhance model evaluation pipelines across development and production.
January 2025
18 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for alan-turing-institute/autoemulate: Delivered customer-facing documentation improvements, strengthened testing infrastructure, resolved a memory-sharing bug, and stabilized dependencies/CI to improve reproducibility and reliability. These efforts reduce onboarding time, increase release confidence, and enhance model evaluation pipelines across development and production.
December 2024
4 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for alan-turing-institute/autoemulate focused on onboarding clarity and installation stability. Delivered targeted documentation improvements to enhance onboarding and usage messaging, and ensured smoother installs by updating dependency constraints. These changes reduced user friction, improved developer experience, and maintained compatibility with core ML tooling.
4 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for alan-turing-institute/autoemulate focused on onboarding clarity and installation stability. Delivered targeted documentation improvements to enhance onboarding and usage messaging, and ensured smoother installs by updating dependency constraints. These changes reduced user friction, improved developer experience, and maintained compatibility with core ML tooling.
December 2024
November 2024
35 Commits • 5 Features
Nov 1, 2024Month 2024-11 — alan-turing-institute/autoemulate: Delivered a focused set of reliability, reproducibility, and documentation improvements that strengthen end-to-end model evaluation and user onboarding. Key features delivered: - Sensitivity analysis workflow improvements: refit model before sensitivity analysis; updated logging to reflect steps. Commits: 580f3408a6d64ee6581408f950ccf6a08c8c6a74; 7c60829c93a83c85ef3f49d02edb5a50e883a807. - Plotting and CV evaluation fixes: resolved double plotting in notebooks; corrected R2 values in plot_cv with proper subsetting; ensured CV consistency across models by seeding. Commits: 75b4bb8ebe0e7d664657ffbcb071d95de7964ac6; 4dd28c46a1b5d9cc44b535078779d5681a00da56; 421b56e2b05da923066779543d4d729d5ea8d67f. - Plotting quality improvements: updated label sizing/labels and adjusted plot_eval output formatting. Commits: 44d7f8bba40f3359da2a49989b70ac0cb806e701; 148e942978223d3222fba6189de41ae5cd650f43. - Documentation and onboarding refresh: README, Quickstart, API docs, contributor guides, branding assets, FAQ updates, changelog and tutorials. (multiple commits). - Code quality and packaging improvements: refactors to enable pickling kernel code, test UI refactor, cleanup of Bayes search usage; version and project metadata updates for release consistency. Commits include: 2a284187b237053c6cf2a11cdf3d4469bb33db7e; 996944cd714ac0538d83d31c31b93bf7a59893c1; af32ac664ae8de51646d246e7ee30ed879313db7; bfcfbdadec12cab734cdd87286df11ea981e52ce; c80fbec6d8c442788c139139ab8168588cde23e0; a411e248f5b81bbb91844b2856076f81544db022. Major bugs fixed: - Fixed double plotting in notebooks; corrected R2s in plot_cv due to improper data subsetting; added random_state to ensure CV equivalence across models. Commits: 75b4bb8ebe0e7d664657ffbcb071d95de7964ac6; 4dd28c46a1b5d9cc44b535078779d5681a00da56; 421b56e2b05da923066779543d4d729d5ea8d67f. - UI/content clarity improvements; updated README title and artifacts. Commits: 9fd28ff79e9e64b8ac7cbb9f56c390505a83691d; b5c07a148458fe46c8c2dc15cb91995da97aaab6. - Version/metadata updates to reflect release. Commits: bfcfbdadec12cab734cdd87286df11ea981e52ce; c80fbec6d8c442788c139139ab8168588cde23e0; a411e248f5b81bbb91844b2856076f81544db022. Overall impact and accomplishments: - Increased reliability and reproducibility of evaluation workflows; improved onboarding and contributor experience; prepared the project for a release with clean metadata and extensive docs. Technologies/skills demonstrated: - Python data science stack, reproducibility with seed control, plotting improvements, code refactoring for pickling, logging enhancements, and comprehensive documentation tooling.
November 2024
35 Commits • 5 Features
Nov 1, 2024Month 2024-11 — alan-turing-institute/autoemulate: Delivered a focused set of reliability, reproducibility, and documentation improvements that strengthen end-to-end model evaluation and user onboarding. Key features delivered: - Sensitivity analysis workflow improvements: refit model before sensitivity analysis; updated logging to reflect steps. Commits: 580f3408a6d64ee6581408f950ccf6a08c8c6a74; 7c60829c93a83c85ef3f49d02edb5a50e883a807. - Plotting and CV evaluation fixes: resolved double plotting in notebooks; corrected R2 values in plot_cv with proper subsetting; ensured CV consistency across models by seeding. Commits: 75b4bb8ebe0e7d664657ffbcb071d95de7964ac6; 4dd28c46a1b5d9cc44b535078779d5681a00da56; 421b56e2b05da923066779543d4d729d5ea8d67f. - Plotting quality improvements: updated label sizing/labels and adjusted plot_eval output formatting. Commits: 44d7f8bba40f3359da2a49989b70ac0cb806e701; 148e942978223d3222fba6189de41ae5cd650f43. - Documentation and onboarding refresh: README, Quickstart, API docs, contributor guides, branding assets, FAQ updates, changelog and tutorials. (multiple commits). - Code quality and packaging improvements: refactors to enable pickling kernel code, test UI refactor, cleanup of Bayes search usage; version and project metadata updates for release consistency. Commits include: 2a284187b237053c6cf2a11cdf3d4469bb33db7e; 996944cd714ac0538d83d31c31b93bf7a59893c1; af32ac664ae8de51646d246e7ee30ed879313db7; bfcfbdadec12cab734cdd87286df11ea981e52ce; c80fbec6d8c442788c139139ab8168588cde23e0; a411e248f5b81bbb91844b2856076f81544db022. Major bugs fixed: - Fixed double plotting in notebooks; corrected R2s in plot_cv due to improper data subsetting; added random_state to ensure CV equivalence across models. Commits: 75b4bb8ebe0e7d664657ffbcb071d95de7964ac6; 4dd28c46a1b5d9cc44b535078779d5681a00da56; 421b56e2b05da923066779543d4d729d5ea8d67f. - UI/content clarity improvements; updated README title and artifacts. Commits: 9fd28ff79e9e64b8ac7cbb9f56c390505a83691d; b5c07a148458fe46c8c2dc15cb91995da97aaab6. - Version/metadata updates to reflect release. Commits: bfcfbdadec12cab734cdd87286df11ea981e52ce; c80fbec6d8c442788c139139ab8168588cde23e0; a411e248f5b81bbb91844b2856076f81544db022. Overall impact and accomplishments: - Increased reliability and reproducibility of evaluation workflows; improved onboarding and contributor experience; prepared the project for a release with clean metadata and extensive docs. Technologies/skills demonstrated: - Python data science stack, reproducibility with seed control, plotting improvements, code refactoring for pickling, logging enhancements, and comprehensive documentation tooling.
October 2024
5 Commits • 3 Features
Oct 1, 2024October 2024 monthly summary for alan-turing-institute/autoemulate: focused on delivering robust sensitivity-analysis enhancements, automated problem inference, and CI/coverage reliability improvements. The work improved analysis flexibility, reduced setup overhead, and strengthened test coverage, driving reliability and faster experimentation for users.
5 Commits • 3 Features
Oct 1, 2024October 2024 monthly summary for alan-turing-institute/autoemulate: focused on delivering robust sensitivity-analysis enhancements, automated problem inference, and CI/coverage reliability improvements. The work improved analysis flexibility, reduced setup overhead, and strengthened test coverage, driving reliability and faster experimentation for users.
October 2024
Activity
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Quality Metrics
Correctness93.2%
Maintainability94.2%
Architecture89.6%
Performance90.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashJupyter NotebookMarkdownPythonRSTSQLShellTOMLYAMLpython
Technical Skills
API DesignCI/CDCode RefactoringConfigurationConfiguration ManagementContribution GuidelinesCross-ValidationData AnalysisData PreprocessingData ScienceData ValidationData VisualizationData generationDataFramesDebugging
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