alan-turing-institute/autoemulate
Mar 2025 – Jul 2025
5 Months active
Languages Used
JinjaJupyter NotebookPythonSQLTOML
Technical Skills
API DesignActive LearningClass RegistryCode FormattingCode IntrospectionCode Refactoring
cisprague
PROFILE
Cisprague
Christopher Iliffe Sprague developed core active learning and uncertainty quantification frameworks for the alan-turing-institute/autoemulate repository, focusing on robust emulation and streamlined user onboarding. He designed and implemented ensemble modeling and Gaussian process modules using Python and PyTorch, enabling predictive mean and covariance outputs for uncertainty-aware workflows. His work included refactoring class hierarchies, enhancing API flexibility, and integrating new backends such as DropoutTorchBackend to support extensible modeling. Christopher also prioritized code quality through comprehensive documentation, type hinting, and linting, ensuring maintainability. These contributions expanded modeling capabilities, improved performance analysis, and reduced onboarding time for users and downstream development teams.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
40Total
Bugs
1
Commits
40
Features
11
Lines of code
23,380
Activity Months5
Your Network
17 people
Work History
July 2025
7 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for alan-turing-institute/autoemulate: Focused on expanding Gaussian-like support, API/backends, and code quality to deliver measurable business value through better predictability, flexibility, and maintainability. Delivered features include Gaussian-like handling enhancements in Ensemble (GaussianEmulator inheritance; standardized Gaussian-like output handling; generalized GaussianLike type checks), API enhancements for Ensemble to accept a sequence of emulators and integration of DropoutTorchBackend (with alignment of DropoutEnsemble and MLP), and substantial documentation and linting improvements. These changes improve uncertainty estimation capabilities, backend extensibility, and developer velocity by reducing onboarding time and maintenance risk. Technologies demonstrated include PyTorch backend integration, object-oriented extension of Ensemble, type-system refinements, and proactive code quality practices.
7 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for alan-turing-institute/autoemulate: Focused on expanding Gaussian-like support, API/backends, and code quality to deliver measurable business value through better predictability, flexibility, and maintainability. Delivered features include Gaussian-like handling enhancements in Ensemble (GaussianEmulator inheritance; standardized Gaussian-like output handling; generalized GaussianLike type checks), API enhancements for Ensemble to accept a sequence of emulators and integration of DropoutTorchBackend (with alignment of DropoutEnsemble and MLP), and substantial documentation and linting improvements. These changes improve uncertainty estimation capabilities, backend extensibility, and developer velocity by reducing onboarding time and maintenance risk. Technologies demonstrated include PyTorch backend integration, object-oriented extension of Ensemble, type-system refinements, and proactive code quality practices.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 focused on delivering ensemble-based uncertainty quantification for Autoemulate. Implemented an ensemble modeling framework (MLPEmulator and Ensemble) to produce mean and covariance predictions, enabling uncertainty-aware emulation. Extended base and stream learners to support ensemble predictions with MultivariateNormal outputs, complemented by training and visualization examples. Updated documentation and notebooks to demonstrate ensemble usage and the benefits of uncertainty-aware modeling, establishing a foundation for robust prognosis and scenario analysis in automated workflows.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 focused on delivering ensemble-based uncertainty quantification for Autoemulate. Implemented an ensemble modeling framework (MLPEmulator and Ensemble) to produce mean and covariance predictions, enabling uncertainty-aware emulation. Extended base and stream learners to support ensemble predictions with MultivariateNormal outputs, complemented by training and visualization examples. Updated documentation and notebooks to demonstrate ensemble usage and the benefits of uncertainty-aware modeling, establishing a foundation for robust prognosis and scenario analysis in automated workflows.
May 2025
6 Commits • 1 Features
May 1, 2025Month: 2025-05 — Concise monthly summary focusing on delivering a cohesive Gaussian distributions framework within the autoemulate project, with refactoring for robust initialization and dense conversion, plus benchmarking and tutorial enhancements to accelerate adoption and user understanding. Emphasis on business value: expanded modeling capabilities, improved performance visibility, and streamlined onboarding for users and downstream teams.
6 Commits • 1 Features
May 1, 2025Month: 2025-05 — Concise monthly summary focusing on delivering a cohesive Gaussian distributions framework within the autoemulate project, with refactoring for robust initialization and dense conversion, plus benchmarking and tutorial enhancements to accelerate adoption and user understanding. Emphasis on business value: expanded modeling capabilities, improved performance visibility, and streamlined onboarding for users and downstream teams.
May 2025
April 2025
6 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for alan-turing-institute/autoemulate. Focused on delivering business value through user-facing documentation improvements, API robustness, and stable dependencies that enable reliable stream-based workflows with simulators/emulators.
April 2025
6 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for alan-turing-institute/autoemulate. Focused on delivering business value through user-facing documentation improvements, API robustness, and stable dependencies that enable reliable stream-based workflows with simulators/emulators.
March 2025
19 Commits • 4 Features
Mar 1, 2025Performance summary for 2025-03 for alan-turing-institute/autoemulate. Delivered foundational Active Learning Framework covering abstract simulators/emulators, concrete projectile and Gaussian Process emulators, a learners registry, and core strategies. Launched comprehensive tutorials, documentation, and examples illustrating stream-based, uncertainty-based, adaptive threshold, and batch-mode active learning. Strengthened code quality and maintainability through formatting, tests, and pre-commit hygiene, and modernized dependencies to support new tooling.
19 Commits • 4 Features
Mar 1, 2025Performance summary for 2025-03 for alan-turing-institute/autoemulate. Delivered foundational Active Learning Framework covering abstract simulators/emulators, concrete projectile and Gaussian Process emulators, a learners registry, and core strategies. Launched comprehensive tutorials, documentation, and examples illustrating stream-based, uncertainty-based, adaptive threshold, and batch-mode active learning. Strengthened code quality and maintainability through formatting, tests, and pre-commit hygiene, and modernized dependencies to support new tooling.
March 2025
Activity
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Quality Metrics
Correctness90.4%
Maintainability90.2%
Architecture90.6%
Performance85.0%
AI Usage21.4%
Skills & Technologies
Programming Languages
JinjaJupyter NotebookPythonSQLTOML
Technical Skills
API DesignActive LearningClass RegistryCode FormattingCode IntrospectionCode RefactoringData AnalysisData ScienceData StructuresData VisualizationDeep LearningDependency ManagementDocumentationEnsemble MethodsGaussian Processes
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