experimental-design/bofire
Feb 2025 – Jun 2026
9 Months active
Languages Used
PythonSQL
Technical Skills
Data VisualizationGaussian ProcessesMachine LearningPlotlyPythonUnit Testing
Jimbo994
PROFILE
Jimbo994
Over a nine-month period, contributed to the experimental-design/bofire repository by developing advanced machine learning features and improving model robustness for experimental design workflows. Built and integrated Gaussian Process surrogates, including robust and pairwise variants, and introduced interactive data visualizations using Plotly to enhance model interpretability. Migrated the codebase to Pydantic v2 for improved data validation and serialization, and implemented new kernel methods such as the Exact Wasserstein Kernel. Leveraged Python, SQL, and continuous integration practices to ensure reliability, while strengthening testing, documentation, and infrastructure. The work enabled more accurate, reproducible optimization and streamlined deployment for data-driven experimentation.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
15Total
Bugs
2
Commits
15
Features
13
Lines of code
89,031
Activity Months9
Your Network
5 peopleWork History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary: Delivered the Exact Wasserstein Kernel for the experimental-design/bofire project, replacing the deprecated PieceWiseLinearGP and enabling precise Wasserstein distance calculations. Implemented lengthscale constraints and restructured the kernel as a FeatureSpecificKernel. Improved interpolation with soft clamp, updated scaler validation, and migrated tests and documentation to reflect the new kernel. Prepared changelog entries and reviewer-alignment notes to support ongoing maintenance.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary: Delivered the Exact Wasserstein Kernel for the experimental-design/bofire project, replacing the deprecated PieceWiseLinearGP and enabling precise Wasserstein distance calculations. Implemented lengthscale constraints and restructured the kernel as a FeatureSpecificKernel. Improved interpolation with soft clamp, updated scaler validation, and migrated tests and documentation to reflect the new kernel. Prepared changelog entries and reviewer-alignment notes to support ongoing maintenance.
June 2026
May 2026
2 Commits • 2 Features
May 1, 2026May 2026: Major feature and stability enhancements in experimental-design/bofire, delivering a robust pairwise preference optimization workflow and reproducible categorical aggregation. Implemented an end-to-end PairwiseGPSurrogate integrated with BoTorch to learn latent utilities from pairwise comparisons, with configurable likelihood (probit/logit), noise priors, and improved GP fitting. Unified surrogate infrastructure (get_feature_indices, engineered_features, and feature-index mappings) to simplify kernel specialization and improve multi-task GP behavior. Added CategoricalOutput handling in aggregate_by_duplicates with majority voting and a seeded random_state for reproducibility. Strengthened tests, documentation, and changelog updates, aligning defaults with BoTorch and improving CI stability. Overall business value: more accurate, reliable preference-based optimization for design experiments, with reproducible results and clearer instrumentation for future iteration.
May 2026
2 Commits • 2 Features
May 1, 2026May 2026: Major feature and stability enhancements in experimental-design/bofire, delivering a robust pairwise preference optimization workflow and reproducible categorical aggregation. Implemented an end-to-end PairwiseGPSurrogate integrated with BoTorch to learn latent utilities from pairwise comparisons, with configurable likelihood (probit/logit), noise priors, and improved GP fitting. Unified surrogate infrastructure (get_feature_indices, engineered_features, and feature-index mappings) to simplify kernel specialization and improve multi-task GP behavior. Added CategoricalOutput handling in aggregate_by_duplicates with majority voting and a seeded random_state for reproducibility. Strengthened tests, documentation, and changelog updates, aligning defaults with BoTorch and improving CI stability. Overall business value: more accurate, reliable preference-based optimization for design experiments, with reproducible results and clearer instrumentation for future iteration.
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
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Quality Metrics
Correctness91.4%
Maintainability84.0%
Architecture85.4%
Performance81.4%
AI Usage28.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Bayesian OptimizationBenchmarkingBug FixingCode RefactoringContinuous IntegrationCross-ValidationData AnalysisData ModelingData ScienceData ValidationData VisualizationGaussian ProcessesGaussian processesJupyterKernel Methods
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