experimental-design/bofire
Feb 2025 – Mar 2026
7 Months active
Languages Used
PythonSQL
Technical Skills
Data VisualizationGaussian ProcessesMachine LearningPlotlyPythonUnit Testing
Jimbo994
PROFILE
Jimbo994
Jim Boelrijk contributed to the experimental-design/bofire repository by developing robust Gaussian Process surrogate modeling features, interactive data visualizations, and infrastructure improvements. He implemented configurable noise constraints and slice visualizations for GP models, enabling users to analyze model predictions and uncertainties across mixed data types. Using Python, Plotly, and Pydantic, Jim enhanced cross-validation workflows, improved data validation, and migrated the codebase to Pydantic v2 for future compatibility. He also addressed critical bugs in optimization logic and strengthened test coverage, documentation, and CI/CD pipelines. His work demonstrated depth in machine learning, data modeling, and software engineering, supporting reliable experimental design.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
12Total
Bugs
2
Commits
12
Features
10
Lines of code
85,841
Activity Months7
Your Network
5 peopleWork History
March 2026
2 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for experimental-design/bofire: Delivered two key features strengthening surrogate modeling and visualization. Focused on robustness, accuracy, and mixed-type data support. Updated changelog to reflect user-facing enhancements. Demonstrated collaboration and code quality through targeted commits.
2 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for experimental-design/bofire: Delivered two key features strengthening surrogate modeling and visualization. Focused on robustness, accuracy, and mixed-type data support. Updated changelog to reflect user-facing enhancements. Demonstrated collaboration and code quality through targeted commits.
March 2026
February 2026
4 Commits • 4 Features
Feb 1, 2026February 2026 monthly summary for experimental-design/bofire. Focused on delivering robust modeling features, improving usability, and strengthening the project’s deployment and testing infrastructure. Key wins include noise-handling enhancements in PiecewiseLinearGPSurrogate, the introduction of weighted mean features, updates to BoFire documentation and tutorials, and infrastructure improvements for CI/CD and benchmarking. These efforts reduced complexity, improved data fit and robustness, and provided clearer pathways for experimentation and deployment.
February 2026
4 Commits • 4 Features
Feb 1, 2026February 2026 monthly summary for experimental-design/bofire. Focused on delivering robust modeling features, improving usability, and strengthening the project’s deployment and testing infrastructure. Key wins include noise-handling enhancements in PiecewiseLinearGPSurrogate, the introduction of weighted mean features, updates to BoFire documentation and tutorials, and infrastructure improvements for CI/CD and benchmarking. These efforts reduced complexity, improved data fit and robustness, and provided clearer pathways for experimentation and deployment.
September 2025
1 Commits
Sep 1, 2025September 2025: Delivered a critical bug fix to NChooseK repair logic in LinearProjection within experimental-design/bofire, improved repair accuracy, and extended test coverage. The change ensures correct zeroing or min-delta adjustments based on sorted values, reducing mis-repairs in optimization steps. Added a unit test for _do_nchoose_k with a defined domain and constraints and linked to commit b02543fae3569a931a35ba4202f3325f4ec7ac27 (suggestions to NChooseK repair #633).
1 Commits
Sep 1, 2025September 2025: Delivered a critical bug fix to NChooseK repair logic in LinearProjection within experimental-design/bofire, improved repair accuracy, and extended test coverage. The change ensures correct zeroing or min-delta adjustments based on sorted values, reducing mis-repairs in optimization steps. Added a unit test for _do_nchoose_k with a defined domain and constraints and linked to commit b02543fae3569a931a35ba4202f3325f4ec7ac27 (suggestions to NChooseK repair #633).
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for experimental-design/bofire: Delivered a Pydantic v2 migration and validation stabilization. Replaced deprecated validators (validator -> field_validator) and encoders (json_encoders -> field_serializer), updated type annotations, and adjusted configurations to preserve data validation and serialization stability across the codebase. Achieved cleaner, forward-compatible models with reduced deprecation risk.
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for experimental-design/bofire: Delivered a Pydantic v2 migration and validation stabilization. Replaced deprecated validators (validator -> field_validator) and encoders (json_encoders -> field_serializer), updated type annotations, and adjusted configurations to preserve data validation and serialization stability across the codebase. Achieved cleaner, forward-compatible models with reduced deprecation risk.
July 2025
2 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Delivered robust Gaussian Process surrogate capabilities in BoFire and fixed a critical naming typo to improve reliability and maintainability. The work focused on enhancing model robustness to outliers, improving numerical priors handling, and strengthening test coverage and documentation. This supports more trustworthy design optimization decisions and reproducibility across experiments.
2 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Delivered robust Gaussian Process surrogate capabilities in BoFire and fixed a critical naming typo to improve reliability and maintainability. The work focused on enhancing model robustness to outliers, improving numerical priors handling, and strengthening test coverage and documentation. This supports more trustworthy design optimization decisions and reproducibility across experiments.
July 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for experimental-design/bofire. Delivered a new interactive cross-validation (CV) folds visualization using Plotly, enabling analysts to compare predicted vs. true values across CV folds with optional uncertainties, labcodes, and input features. The UI includes a dropdown to select individual folds or view all folds together, facilitating performance analysis across data splits. This feature enhances model debugging, explainability, and decision-making for model selection. Implemented the core visualization through plot_cv_folds_plotly and wired it into the existing CV workflow. Business value realized: faster, deeper insights into data splits and model performance.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for experimental-design/bofire. Delivered a new interactive cross-validation (CV) folds visualization using Plotly, enabling analysts to compare predicted vs. true values across CV folds with optional uncertainties, labcodes, and input features. The UI includes a dropdown to select individual folds or view all folds together, facilitating performance analysis across data splits. This feature enhances model debugging, explainability, and decision-making for model selection. Implemented the core visualization through plot_cv_folds_plotly and wired it into the existing CV workflow. Business value realized: faster, deeper insights into data splits and model performance.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered GP Model Slice Visualization feature for experimental-design/bofire. Implemented plot_gp_slice_plotly to visualize Gaussian Process predictions and uncertainties by fixing certain inputs and varying others, enabling inspection of the GP response surface and its uncertainties. Added accompanying unit tests to ensure correctness. Committed work highlights include f79004d3d2a06fc45b3cc25e2db32f93cb9b53e1 ("added plot_gp_slice.py (#499)").
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered GP Model Slice Visualization feature for experimental-design/bofire. Implemented plot_gp_slice_plotly to visualize Gaussian Process predictions and uncertainties by fixing certain inputs and varying others, enabling inspection of the GP response surface and its uncertainties. Added accompanying unit tests to ensure correctness. Committed work highlights include f79004d3d2a06fc45b3cc25e2db32f93cb9b53e1 ("added plot_gp_slice.py (#499)").
February 2025
Activity
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Quality Metrics
Correctness90.8%
Maintainability85.0%
Architecture85.0%
Performance81.6%
AI Usage23.4%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Bayesian OptimizationBenchmarkingBug FixingCode RefactoringContinuous IntegrationCross-ValidationData AnalysisData ModelingData ScienceData ValidationData VisualizationGaussian ProcessesGaussian processesJupyterMachine Learning
Repositories Contributed To
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