Sam Greenbury developed core modeling and data transformation capabilities for the alan-turing-institute/autoemulate repository, focusing on robust emulator frameworks and uncertainty quantification. He engineered extensible APIs and refactored emulator and transform base classes to support advanced workflows, including spatiotemporal data and probabilistic modeling. Using Python and PyTorch, Sam implemented covariance-aware prediction, device-agnostic execution, and batch processing utilities, while integrating CI/CD and comprehensive testing for reliability. His work addressed shape propagation, model reinitialization, and data pipeline enhancements, enabling reproducible experiments and scalable ML pipelines. The depth of engineering is reflected in modular design, maintainable code, and thorough test-driven development practices.
October 2025 highlights for alan-turing-institute/autoemulate: delivered probabilistic modeling enhancements, API and demo improvements, strengthened uncertainty quantification, and robust CI/CD, testing, and registry architecture. This work increases modeling fidelity, reduces time-to-release, and improves developer experience. Key outcomes include the beta-distribution MLP and ZeroOneInflatedBeta refactor, API enhancements (parameters_range, supports_uq), emulator/UQ improvements, comprehensive testing and CI hardening, and GP/Emulator registry enhancements with bug fixes.
October 2025 highlights for alan-turing-institute/autoemulate: delivered probabilistic modeling enhancements, API and demo improvements, strengthened uncertainty quantification, and robust CI/CD, testing, and registry architecture. This work increases modeling fidelity, reduces time-to-release, and improves developer experience. Key outcomes include the beta-distribution MLP and ZeroOneInflatedBeta refactor, API enhancements (parameters_range, supports_uq), emulator/UQ improvements, comprehensive testing and CI hardening, and GP/Emulator registry enhancements with bug fixes.
September 2025 monthly summary for alan-turing-institute/autoemulate. Focused on delivering core modeling capabilities, stabilizing the codebase, and tightening the CI/dev-ops pipeline to accelerate business value and reliability. Key features delivered: - Covariance support and tests: Added covariance support with a refactor and updated tests; tests for using input covariance implemented (commits 2bd3e28a80a3... and 994bb6cb027c...). - Spatiotemporal support and CI integration: Added spatiotemporal dependencies and wired spatiotemporal checks into pre-commit and CI workflows (commits 189f79a9dc63... and f073a0fd596f...). - Reaction Diffusion data integration and FNO support: Introduced ReactionDiffusionDataset (init from data), data reshaping method, updated FNO emulator init, and started a Reaction Diffusion notebook (commits 0b55a4bfe4a7..., b92ba29f42ca..., 786eaa45e5f9..., 5c39e4489408...). - Mean-only calculation support and predict_mean override: Added mean-only calculation support and expose predict_mean in overrides (commits 17101457c96c4... and 3402fd785a33...). - Notebook benchmarking approaches and API refinements: Implemented notebook benchmarking approaches and continued API refinement (commit 5ced2af25a50...). Major bugs fixed: - Affine detection fixes and validation: Corrected affine detection logic, added tests, and refined exclusion conditions when all terms are affine (commits 631226754265..., c379fcc044da..., 1f7755f1927c...). - Device handling and test stability: Fixed device handling issues in tests (commit cbf04c24f554...). - Polynomial regression tests and related stability: Fixed polynomial regression tests and related comments (commit 57b0313887...). - Pre-commit/CI quality: Resolved pre-commit and lint issues and updated tooling (commits da57c8413bb3... and 3f8d20fb0a70...). - Test robustness and xfails: Improved test robustness, refined xfail handling and related test expectations (commits ce09b5ee9afa..., 9ba4ddfd73a3..., ea324324345f...). Overall impact and accomplishments: - Substantial advancement in modeling fidelity and reliability, enabling more accurate uncertainty quantification through covariance and spatiotemporal support. - Data pipeline enhancements (ReactionDiffusionDataset) and FNO emulator expansion create a more expressive framework for advanced PDE-informed ML tasks. - Improved performance and scalability via Jacobian caching and conditional mean computation, alongside robust test coverage and CI quality. - Clear business value: faster iteration cycles, more trustworthy model predictions, and an integrated notebook experience that demonstrates end-to-end workflows for researchers and engineers. Technologies/skills demonstrated: - Python, project refactoring and API design, unit/integration tests, and test-driven development. - Spatiotemporal dependencies, CI/pre-commit integration, and type hints enhancements. - Reaction-Diffusion data handling, FNO emulation, and notebook-based experimentation. - Performance optimizations (Jacobian caching, conditional mean) and robust benchmarking workflows. - Documentation and notebook improvements, with emphasis on clarity and maintainability.
September 2025 monthly summary for alan-turing-institute/autoemulate. Focused on delivering core modeling capabilities, stabilizing the codebase, and tightening the CI/dev-ops pipeline to accelerate business value and reliability. Key features delivered: - Covariance support and tests: Added covariance support with a refactor and updated tests; tests for using input covariance implemented (commits 2bd3e28a80a3... and 994bb6cb027c...). - Spatiotemporal support and CI integration: Added spatiotemporal dependencies and wired spatiotemporal checks into pre-commit and CI workflows (commits 189f79a9dc63... and f073a0fd596f...). - Reaction Diffusion data integration and FNO support: Introduced ReactionDiffusionDataset (init from data), data reshaping method, updated FNO emulator init, and started a Reaction Diffusion notebook (commits 0b55a4bfe4a7..., b92ba29f42ca..., 786eaa45e5f9..., 5c39e4489408...). - Mean-only calculation support and predict_mean override: Added mean-only calculation support and expose predict_mean in overrides (commits 17101457c96c4... and 3402fd785a33...). - Notebook benchmarking approaches and API refinements: Implemented notebook benchmarking approaches and continued API refinement (commit 5ced2af25a50...). Major bugs fixed: - Affine detection fixes and validation: Corrected affine detection logic, added tests, and refined exclusion conditions when all terms are affine (commits 631226754265..., c379fcc044da..., 1f7755f1927c...). - Device handling and test stability: Fixed device handling issues in tests (commit cbf04c24f554...). - Polynomial regression tests and related stability: Fixed polynomial regression tests and related comments (commit 57b0313887...). - Pre-commit/CI quality: Resolved pre-commit and lint issues and updated tooling (commits da57c8413bb3... and 3f8d20fb0a70...). - Test robustness and xfails: Improved test robustness, refined xfail handling and related test expectations (commits ce09b5ee9afa..., 9ba4ddfd73a3..., ea324324345f...). Overall impact and accomplishments: - Substantial advancement in modeling fidelity and reliability, enabling more accurate uncertainty quantification through covariance and spatiotemporal support. - Data pipeline enhancements (ReactionDiffusionDataset) and FNO emulator expansion create a more expressive framework for advanced PDE-informed ML tasks. - Improved performance and scalability via Jacobian caching and conditional mean computation, alongside robust test coverage and CI quality. - Clear business value: faster iteration cycles, more trustworthy model predictions, and an integrated notebook experience that demonstrates end-to-end workflows for researchers and engineers. Technologies/skills demonstrated: - Python, project refactoring and API design, unit/integration tests, and test-driven development. - Spatiotemporal dependencies, CI/pre-commit integration, and type hints enhancements. - Reaction-Diffusion data handling, FNO emulation, and notebook-based experimentation. - Performance optimizations (Jacobian caching, conditional mean) and robust benchmarking workflows. - Documentation and notebook improvements, with emphasis on clarity and maintainability.
Monthly summary for 2025-08 (alan-turing-institute/autoemulate): Delivered a suite of API refinements, robustness fixes, and data-capability enhancements that improve reliability, onboarding, and value realization for ML pipelines. Key work includes consolidating emulator base-class relationships and enabling a Transform superclass, adding load_model and reinitialize/refit workflows, broadening support for transformed outputs, and expanding data handling capabilities. Reliability improvements address silent failures and shape propagation issues, while testing and CI updates improve stability across the suite. Demonstrated proficiency in API design, Python-based ML tooling, and data-centric experimentation with FNO and spatio-temporal datasets.
Monthly summary for 2025-08 (alan-turing-institute/autoemulate): Delivered a suite of API refinements, robustness fixes, and data-capability enhancements that improve reliability, onboarding, and value realization for ML pipelines. Key work includes consolidating emulator base-class relationships and enabling a Transform superclass, adding load_model and reinitialize/refit workflows, broadening support for transformed outputs, and expanding data handling capabilities. Reliability improvements address silent failures and shape propagation issues, while testing and CI updates improve stability across the suite. Demonstrated proficiency in API design, Python-based ML tooling, and data-centric experimentation with FNO and spatio-temporal datasets.
July 2025 monthly summary for alan-turing-institute/autoemulate. This period focused on delivering core GP/MLE capabilities with improved stability, test determinism, and scalable API design, while grounding experimentation in robust, maintainable code.
July 2025 monthly summary for alan-turing-institute/autoemulate. This period focused on delivering core GP/MLE capabilities with improved stability, test determinism, and scalable API design, while grounding experimentation in robust, maintainable code.
June 2025 monthly summary for alan-turing-institute/autoemulate: Delivered a set of business-value features and robustness improvements across the transformed-emulation stack, expanded test coverage, and strengthened integration with devices and GP-based emulation. The work focused on delivering tangible capabilities for users and improving stability, performance, and maintainability of the project. Key features delivered include a user-facing progress indicator with the ability to re-run notebooks, introduction of an initial standardize transform with architecture refactor, and enhancements to the transformed emulator initialization and matrix handling. Collectively, these changes enable more reliable data transforms, faster iterations in notebooks, and a more extensible emulator framework for varied output types. Major bugs fixed encompassed import issues, improved error messaging, handling edge cases in standard deviation calculations, and maintenance fixes to ensure pre-commit and API cleanliness. These fixes reduce unexpected failures and improve developer experience during review and contribution cycles. Overall impact and accomplishments: TheJune cycle delivered a more stable, test-covered, and developer-friendly autoemulate pipeline. The ecosystem now supports richer transform pipelines, robust PCA and sampling utilities, and expanded emulator coverage with device support. Versioning was updated to reflect maturation (v0.3.3), signaling readiness for broader adoption and downstream integration with analytics workflows. Technologies/skills demonstrated: Python data transformation patterns, object-oriented refactoring (base classes and mixins), PCA and sampling techniques, GaussianProcess-like emulation, PyTorch backend enhancements, no_grad usage for performance, expanded notebook tooling, and comprehensive testing strategies (unit, integration, and targeted end-to-end scenarios).
June 2025 monthly summary for alan-turing-institute/autoemulate: Delivered a set of business-value features and robustness improvements across the transformed-emulation stack, expanded test coverage, and strengthened integration with devices and GP-based emulation. The work focused on delivering tangible capabilities for users and improving stability, performance, and maintainability of the project. Key features delivered include a user-facing progress indicator with the ability to re-run notebooks, introduction of an initial standardize transform with architecture refactor, and enhancements to the transformed emulator initialization and matrix handling. Collectively, these changes enable more reliable data transforms, faster iterations in notebooks, and a more extensible emulator framework for varied output types. Major bugs fixed encompassed import issues, improved error messaging, handling edge cases in standard deviation calculations, and maintenance fixes to ensure pre-commit and API cleanliness. These fixes reduce unexpected failures and improve developer experience during review and contribution cycles. Overall impact and accomplishments: TheJune cycle delivered a more stable, test-covered, and developer-friendly autoemulate pipeline. The ecosystem now supports richer transform pipelines, robust PCA and sampling utilities, and expanded emulator coverage with device support. Versioning was updated to reflect maturation (v0.3.3), signaling readiness for broader adoption and downstream integration with analytics workflows. Technologies/skills demonstrated: Python data transformation patterns, object-oriented refactoring (base classes and mixins), PCA and sampling techniques, GaussianProcess-like emulation, PyTorch backend enhancements, no_grad usage for performance, expanded notebook tooling, and comprehensive testing strategies (unit, integration, and targeted end-to-end scenarios).
May 2025 highlights for alan-turing-institute/autoemulate include architectural refactors, broad device support, and enhanced modeling capabilities that collectively improve maintainability, scalability, and numerical robustness. The month focused on refactoring core emulator and input handling, enabling device-aware execution across components, advancing the transforms and VAE toolset, and modernizing the tuner/API surfaces, supported by strengthened tests and CI hygiene.
May 2025 highlights for alan-turing-institute/autoemulate include architectural refactors, broad device support, and enhanced modeling capabilities that collectively improve maintainability, scalability, and numerical robustness. The month focused on refactoring core emulator and input handling, enabling device-aware execution across components, advancing the transforms and VAE toolset, and modernizing the tuner/API surfaces, supported by strengthened tests and CI hygiene.
April 2025 — Consolidated reliability and scalability for alan-turing-institute/autoemulate. Focused on stabilizing the codebase, expanding test coverage, and tightening CI/CD alignment to deliver measurable business value. Delivered key features, targeted bug fixes, and tooling improvements that reduce maintenance cost and improve predictability of experiments and deployments across GP backends, standardization, and data preprocessing components.
April 2025 — Consolidated reliability and scalability for alan-turing-institute/autoemulate. Focused on stabilizing the codebase, expanding test coverage, and tightening CI/CD alignment to deliver measurable business value. Delivered key features, targeted bug fixes, and tooling improvements that reduce maintenance cost and improve predictability of experiments and deployments across GP backends, standardization, and data preprocessing components.
2025-03 monthly summary for alan-turing-institute/autoemulate. Delivered major enhancements to training configurability, introduced a multitask GP backend, and restructured the emulator architecture, complemented by internal tooling and CI improvements. These changes increase model training flexibility, enable advanced Gaussian Process modeling, and improve code quality and maintainability, delivering measurable business value from faster experimentation, more robust modeling, and reduced maintenance overhead.
2025-03 monthly summary for alan-turing-institute/autoemulate. Delivered major enhancements to training configurability, introduced a multitask GP backend, and restructured the emulator architecture, complemented by internal tooling and CI improvements. These changes increase model training flexibility, enable advanced Gaussian Process modeling, and improve code quality and maintainability, delivering measurable business value from faster experimentation, more robust modeling, and reduced maintenance overhead.
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