Marjan Famili developed and maintained core features for the alan-turing-institute/autoemulate repository, building a robust simulation and emulation platform for scientific workflows. Over nine months, Marjan delivered enhancements such as modular simulator architectures, advanced MCMC calibration with device placement, and new data handling capabilities, all implemented in Python and PyTorch. Their work included refactoring APIs for maintainability, expanding dashboard visualizations, and integrating uncertainty quantification and sensitivity analysis into modeling pipelines. By improving code quality, documentation, and reproducibility, Marjan enabled scalable experimentation and more reliable model evaluation, demonstrating depth in scientific computing, machine learning, and collaborative software engineering practices.
September 2025 performance summary for alan-turing-institute/autoemulate: Delivered a new dtype parameter for AutoEmulateDataset to standardize tensor data types across inputs, outputs, and constants at initialization/loading. This enhances data consistency, reduces type-related errors, and improves flexibility for downstream modeling pipelines. No major bugs reported this month; primary focus on feature delivery and code quality improvements.
September 2025 performance summary for alan-turing-institute/autoemulate: Delivered a new dtype parameter for AutoEmulateDataset to standardize tensor data types across inputs, outputs, and constants at initialization/loading. This enhances data consistency, reduces type-related errors, and improves flexibility for downstream modeling pipelines. No major bugs reported this month; primary focus on feature delivery and code quality improvements.
2025-08 performance and impact summary: Delivered new simulation outputs access and expanded emulation capabilities, accompanied by broad code quality improvements to enable scalable analytics and safer maintenance. The work strengthens data extraction, accelerates experimentation, and reduces technical debt across the autoemulate codebase.
2025-08 performance and impact summary: Delivered new simulation outputs access and expanded emulation capabilities, accompanied by broad code quality improvements to enable scalable analytics and safer maintenance. The work strengthens data extraction, accelerates experimentation, and reduces technical debt across the autoemulate codebase.
July 2025 monthly summary for alan-turing-institute/autoemulate focusing on business value and technical achievements. Key features delivered include tutorial numbering correction and MCMC calibration enhancements with device placement across CPU/GPU and multi-sampler support. No major bugs fixed this month. Overall impact: improved instructional accuracy and reproducibility, enhanced experimentation across hardware, and greater flexibility in MCMC workflows. Technologies demonstrated: Python, MCMC methods, explicit CPU/GPU device handling, kernel selection refactoring for multiple samplers, experimental notebook updates, and Git-based workflows.
July 2025 monthly summary for alan-turing-institute/autoemulate focusing on business value and technical achievements. Key features delivered include tutorial numbering correction and MCMC calibration enhancements with device placement across CPU/GPU and multi-sampler support. No major bugs fixed this month. Overall impact: improved instructional accuracy and reproducibility, enhanced experimentation across hardware, and greater flexibility in MCMC workflows. Technologies demonstrated: Python, MCMC methods, explicit CPU/GPU device handling, kernel selection refactoring for multiple samplers, experimental notebook updates, and Git-based workflows.
June 2025: Key features delivered, major stability improvements, and clear business value for AutoEmulate. Focus was on accelerating calibration workflows, improving uncertainty quantification, and enhancing developer ergonomics across the repo.
June 2025: Key features delivered, major stability improvements, and clear business value for AutoEmulate. Focus was on accelerating calibration workflows, improving uncertainty quantification, and enhancing developer ergonomics across the repo.
In May 2025, delivered a set of performance-focused feature enhancements for the autoemulate project, with emphasis on faster model evaluation, robust uncertainty quantification, and improved communication of results. The work strengthens decision support by delivering faster history matching, scalable inference, and richer sensitivity analyses, complemented by updated visuals and documentation.
In May 2025, delivered a set of performance-focused feature enhancements for the autoemulate project, with emphasis on faster model evaluation, robust uncertainty quantification, and improved communication of results. The work strengthens decision support by delivering faster history matching, scalable inference, and richer sensitivity analyses, complemented by updated visuals and documentation.
April 2025 summary for alan-turing-institute/autoemulate focused on stabilizing the pipeline, expanding simulator architecture for modularity, and boosting testing, visualization, and CI. Deliverables improved robustness, reliability, and business value across the history-matching workflow, simulator API, dashboard visuals, and testing infrastructure.
April 2025 summary for alan-turing-institute/autoemulate focused on stabilizing the pipeline, expanding simulator architecture for modularity, and boosting testing, visualization, and CI. Deliverables improved robustness, reliability, and business value across the history-matching workflow, simulator API, dashboard visuals, and testing infrastructure.
March 2025: Consolidated AutoEmulate into a more capable, scalable preprocessing and modeling platform with a focus on reproducibility, faster experimentation, and clearer workflows. Delivered end-to-end enhancements across tutorials, pipelines, and evaluation to enable data scientists to experiment with target encoding, uncertainty-aware preprocessing, and history-informed parameter refinement.
March 2025: Consolidated AutoEmulate into a more capable, scalable preprocessing and modeling platform with a focus on reproducibility, faster experimentation, and clearer workflows. Delivered end-to-end enhancements across tutorials, pipelines, and evaluation to enable data scientists to experiment with target encoding, uncertainty-aware preprocessing, and history-informed parameter refinement.
February 2025 performance summary for alan-turing-institute/autoemulate: Focused on delivering core metrics enhancements, expanding the analytical toolkit, and improving reproducibility. Work targeted at business value by enabling more robust validation, predictive analytics, and tutorial-driven adoption of new capabilities.
February 2025 performance summary for alan-turing-institute/autoemulate: Focused on delivering core metrics enhancements, expanding the analytical toolkit, and improving reproducibility. Work targeted at business value by enabling more robust validation, predictive analytics, and tutorial-driven adoption of new capabilities.
Concise monthly summary for 2025-01 focusing on business value and technical achievements in the alan-turing-institute/autoemulate repository.
Concise monthly summary for 2025-01 focusing on business value and technical achievements in the alan-turing-institute/autoemulate repository.
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