experimental-design/bofire
Oct 2024 – Mar 2026
18 Months active
Languages Used
C++PythonJSONJupyter NotebookSQLTOMLYAMLMarkdown
Technical Skills
Bayesian OptimizationGPyTorchKernel MethodsMachine LearningPyTorchPython
Johannes P. Dürholt
PROFILE
Johannes P. Dürholt
Over 18 months, contributed to experimental-design/bofire by building and refining advanced optimization and modeling features for experimental design workflows. Developed surrogate modeling capabilities, constraint systems, and benchmarking frameworks using Python, PyTorch, and BoTorch, with a focus on Bayesian optimization and data modeling. Enhanced the codebase through robust API design, comprehensive testing, and continuous integration improvements, ensuring reliability and scalability. Introduced extensible registration APIs, advanced feature engineering, and compatibility updates for evolving dependencies. The work emphasized maintainability, reproducibility, and interpretability, enabling more flexible experimentation and robust model deployment for users working with complex data and optimization tasks.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
89Total
Bugs
13
Commits
89
Features
45
Lines of code
35,731
Activity Months18
Your Network
5 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for experimental-design/bofire: Implemented a public API to register custom strategies and surrogates in the BotorchSurrogate framework, enabling seamless integration of user-defined types with automatic Pydantic validation and dynamic union rebuilding. The registration pattern was extended to kernels, priors, and engineered features, with decorator-based and programmatic usage supported. Delivered comprehensive documentation and tests to validate registration flows and ensure long-term maintainability.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for experimental-design/bofire: Implemented a public API to register custom strategies and surrogates in the BotorchSurrogate framework, enabling seamless integration of user-defined types with automatic Pydantic validation and dynamic union rebuilding. The registration pattern was extended to kernels, priors, and engineered features, with decorator-based and programmatic usage supported. Delivered comprehensive documentation and tests to validate registration flows and ensure long-term maintainability.
March 2026
February 2026
11 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for experimental-design/bofire. Delivered major enhancements to feature engineering pipelines and a broad stability/compatibility sweep that modernizes the tech stack, improves modelability, and strengthens developer workflow. The work emphasizes business value through richer features, robust testing, and future-proof tooling, enabling faster experimentation and more reliable production models.
February 2026
11 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for experimental-design/bofire. Delivered major enhancements to feature engineering pipelines and a broad stability/compatibility sweep that modernizes the tech stack, improves modelability, and strengthens developer workflow. The work emphasizes business value through richer features, robust testing, and future-proof tooling, enabling faster experimentation and more reliable production models.
January 2026
3 Commits • 2 Features
Jan 1, 20262026-01 Monthly Summary for experimental-design/bofire: Delivered core feature enhancements and stability improvements to support reliable, scalable surrogate modeling and molecular data processing. Key outcomes include ensemble surrogate capabilities for single-task Gaussian processes, dependency stabilization via explicit library pinning, and advanced molecular data handling with new ContinuousMolecularInput and MolecularWeightedSumFeature. All changes include added tests and documentation to ensure maintainability and faster onboarding for downstream teams.
3 Commits • 2 Features
Jan 1, 20262026-01 Monthly Summary for experimental-design/bofire: Delivered core feature enhancements and stability improvements to support reliable, scalable surrogate modeling and molecular data processing. Key outcomes include ensemble surrogate capabilities for single-task Gaussian processes, dependency stabilization via explicit library pinning, and advanced molecular data handling with new ContinuousMolecularInput and MolecularWeightedSumFeature. All changes include added tests and documentation to ensure maintainability and faster onboarding for downstream teams.
January 2026
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025: Delivered two high-impact enhancements in experimental-design/bofire that improve reproducibility, evaluation fidelity, and stability, while expanding test coverage. The work strengthens caching reliability, feature engineering, and benchmarking pipelines, enabling faster iteration with lower regression risk and clearer business value.
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025: Delivered two high-impact enhancements in experimental-design/bofire that improve reproducibility, evaluation fidelity, and stability, while expanding test coverage. The work strengthens caching reliability, feature engineering, and benchmarking pipelines, enabling faster iteration with lower regression risk and clearer business value.
November 2025
3 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — experimental-design/bofire delivered key feature improvements, robust error handling, and enhanced CI workflows. The work focused on business value by clarifying constraint behavior, enabling on-demand CI, and improving stability when no valid experiments are available. These changes reduce debugging time, accelerate iteration cycles, and improve test coverage.
3 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — experimental-design/bofire delivered key feature improvements, robust error handling, and enhanced CI workflows. The work focused on business value by clarifying constraint behavior, enabling on-demand CI, and improving stability when no valid experiments are available. These changes reduce debugging time, accelerate iteration cycles, and improve test coverage.
November 2025
October 2025
5 Commits • 3 Features
Oct 1, 2025October 2025 monthly summary for experimental-design/bofire. Delivered a set of reliability and capability enhancements to the BoFire optimization workflow, with a focus on controlled feature selection, robustness, and pipeline modernization. Highlights include bug fixes that stabilize optimization behavior and feature-weight handling, and feature improvements that broaden optimization strategies and align defaults with industry practices.
October 2025
5 Commits • 3 Features
Oct 1, 2025October 2025 monthly summary for experimental-design/bofire. Delivered a set of reliability and capability enhancements to the BoFire optimization workflow, with a focus on controlled feature selection, robustness, and pipeline modernization. Highlights include bug fixes that stabilize optimization behavior and feature-weight handling, and feature improvements that broaden optimization strategies and align defaults with industry practices.
September 2025
3 Commits • 2 Features
Sep 1, 2025September 2025 performance: Delivered key feature improvements in bofire that enhance benchmarking realism and surrogate modeling capabilities, enabling faster, more reliable evaluation and decision-making for model deployment. Major features delivered include: (1) Enhanced Benchmarking Framework with BoTorch synthetic test functions and spurious feature wrappers, including API imports and test updates; (2) Additive Map Saas Single Task GPS Surrogate added to BoTorch surrogates with extension of AnyBotorchSurrogate to support the new model. Stability and quality efforts included updating benchmarks and tests to cover the new features and ensure API consistency. No major bugs fixed this month; focus was on capability expansion and code quality. Technologies/skills demonstrated: BoTorch integration, synthetic benchmarks, advanced surrogate modeling, Python, API design, test automation, and CI readiness to support scalable experimentation.
3 Commits • 2 Features
Sep 1, 2025September 2025 performance: Delivered key feature improvements in bofire that enhance benchmarking realism and surrogate modeling capabilities, enabling faster, more reliable evaluation and decision-making for model deployment. Major features delivered include: (1) Enhanced Benchmarking Framework with BoTorch synthetic test functions and spurious feature wrappers, including API imports and test updates; (2) Additive Map Saas Single Task GPS Surrogate added to BoTorch surrogates with extension of AnyBotorchSurrogate to support the new model. Stability and quality efforts included updating benchmarks and tests to cover the new features and ensure API consistency. No major bugs fixed this month; focus was on capability expansion and code quality. Technologies/skills demonstrated: BoTorch integration, synthetic benchmarks, advanced surrogate modeling, Python, API design, test automation, and CI readiness to support scalable experimentation.
September 2025
August 2025
6 Commits • 3 Features
Aug 1, 20252025-08 monthly summary for experimental-design/bofire: Delivered key feature enhancements, improved tutorials, and updated documentation to strengthen reliability, usability, and downstream integration. Focused on BoFire compatibility, API quality, and test coverage to enable scalable adoption across teams.
August 2025
6 Commits • 3 Features
Aug 1, 20252025-08 monthly summary for experimental-design/bofire: Delivered key feature enhancements, improved tutorials, and updated documentation to strengthen reliability, usability, and downstream integration. Focused on BoFire compatibility, API quality, and test coverage to enable scalable adoption across teams.
July 2025
4 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for experimental-design/bofire: The team delivered key features and fixed critical issues that improve model reliability, scalability, and business value. Highlights include a bug fix for scalekernel evaluation in SingleTaskGPHyperconfig, enabling correct kernel configurations and test alignment; new priors support with Hvarfner priors in MixedSingleTaskGP; introduction of fully Bayesian modeling options with AdditiveMapSaasSingleTaskGPSurrogate and input warping; and a new capability to compute the number of categorical combinations in Inputs to prevent memory blowups. These changes, along with accompanying tests and refactors for compatibility with newer library versions, position the project to support robust surrogate modeling at scale and easier experimentation with priors and Bayesian inference.
4 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for experimental-design/bofire: The team delivered key features and fixed critical issues that improve model reliability, scalability, and business value. Highlights include a bug fix for scalekernel evaluation in SingleTaskGPHyperconfig, enabling correct kernel configurations and test alignment; new priors support with Hvarfner priors in MixedSingleTaskGP; introduction of fully Bayesian modeling options with AdditiveMapSaasSingleTaskGPSurrogate and input warping; and a new capability to compute the number of categorical combinations in Inputs to prevent memory blowups. These changes, along with accompanying tests and refactors for compatibility with newer library versions, position the project to support robust surrogate modeling at scale and easier experimentation with priors and Bayesian inference.
July 2025
June 2025
7 Commits • 2 Features
Jun 1, 2025June 2025 focused on strengthening constraint handling, model reliability, and interpretability in experimental-design/bofire. Delivered bug fixes, refactoring, and enhancements to feasibility-aware Bayesian optimization, aligning the data model with practical usage and improving decision quality for optimization tasks. The work reduced configuration errors, improved validation, and expanded reporting/interpretability capabilities for end-users and researchers.
June 2025
7 Commits • 2 Features
Jun 1, 2025June 2025 focused on strengthening constraint handling, model reliability, and interpretability in experimental-design/bofire. Delivered bug fixes, refactoring, and enhancements to feasibility-aware Bayesian optimization, aligning the data model with practical usage and improving decision quality for optimization tasks. The work reduced configuration errors, improved validation, and expanded reporting/interpretability capabilities for end-users and researchers.
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05 – This month focused on delivering a key enhancement to the constraint system in experimental-design/bofire, with a new CategoricalExcludeConstraint and Condition-based Constraints, plus integration into the constraint pipeline and support in RandomStrategy. These changes enable more flexible and safe experimental designs by enforcing exclusions across categorical and numeric features, reducing invalid configurations and improving experiment quality. The work is supported by a single commit: 3af8c7df6bc6137e78080644c5a84ffa8e33317c (CategoricalExcludeConstraint (#582)).
1 Commits • 1 Features
May 1, 2025Month: 2025-05 – This month focused on delivering a key enhancement to the constraint system in experimental-design/bofire, with a new CategoricalExcludeConstraint and Condition-based Constraints, plus integration into the constraint pipeline and support in RandomStrategy. These changes enable more flexible and safe experimental designs by enforcing exclusions across categorical and numeric features, reducing invalid configurations and improving experiment quality. The work is supported by a single commit: 3af8c7df6bc6137e78080644c5a84ffa8e33317c (CategoricalExcludeConstraint (#582)).
May 2025
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for experimental-design/bofire focusing on key features delivered, major bugs fixed, and overall impact. Highlights include architecture-level Botorch optimizer refactor with AcquisitionOptimizer base and concrete implementations (BotorchOptimizer, LSRBO), a tensor device alignment bug fix in the Active Learning workflow, and expanded MOBO configurability via new MoboStrategy reference point data models. These changes improve maintainability, runtime robustness, and experimentation velocity, enabling more reliable optimization and easier onboarding for new strategies.
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for experimental-design/bofire focusing on key features delivered, major bugs fixed, and overall impact. Highlights include architecture-level Botorch optimizer refactor with AcquisitionOptimizer base and concrete implementations (BotorchOptimizer, LSRBO), a tensor device alignment bug fix in the Active Learning workflow, and expanded MOBO configurability via new MoboStrategy reference point data models. These changes improve maintainability, runtime robustness, and experimentation velocity, enabling more reliable optimization and easier onboarding for new strategies.
March 2025
9 Commits • 3 Features
Mar 1, 2025March 2025 performance summary for experimental-design/bofire: Core enhancements focused on experimental design robustness, model capabilities for categorical outputs, and developer experience. Delivered Blocking capabilities for Fractional Factorial Designs with validation and integration into existing design utilities; enhanced Bayesian optimization to support categorical outputs and updated molecule benchmarking; and a suite of documentation, CI, and developer experience improvements to streamline contribution, testing, and deployment. These changes reduce confounding, improve modeling accuracy for categorical tasks, and strengthen deployment reliability and onboarding.
9 Commits • 3 Features
Mar 1, 2025March 2025 performance summary for experimental-design/bofire: Core enhancements focused on experimental design robustness, model capabilities for categorical outputs, and developer experience. Delivered Blocking capabilities for Fractional Factorial Designs with validation and integration into existing design utilities; enhanced Bayesian optimization to support categorical outputs and updated molecule benchmarking; and a suite of documentation, CI, and developer experience improvements to streamline contribution, testing, and deployment. These changes reduce confounding, improve modeling accuracy for categorical tasks, and strengthen deployment reliability and onboarding.
March 2025
February 2025
10 Commits • 4 Features
Feb 1, 2025February 2025 monthly summary for experimental-design/bofire. Delivered critical compatibility fixes, new design features, and robust CI improvements, driving reliability, reproducibility, and faster iteration for multi-objective Bayesian optimization workflows.
February 2025
10 Commits • 4 Features
Feb 1, 2025February 2025 monthly summary for experimental-design/bofire. Delivered critical compatibility fixes, new design features, and robust CI improvements, driving reliability, reproducibility, and faster iteration for multi-objective Bayesian optimization workflows.
January 2025
8 Commits • 3 Features
Jan 1, 2025January 2025 performance summary for experimental-design/bofire: Delivered measurable improvements in optimization UX, model flexibility, and platform readiness, enabling faster, more reliable experimentation and easier adoption of newer Python ecosystems. Highlights include opt-in progress visualization for hyperparameter optimization, a major DoE strategy model refactor with enhanced optimization capabilities, more forgiving candidate counting, platform/CI updates to support Python 3.10+ and modern dependencies, corrected desirability calculations for predictive strategies, and stability improvements through mapper tests.
8 Commits • 3 Features
Jan 1, 2025January 2025 performance summary for experimental-design/bofire: Delivered measurable improvements in optimization UX, model flexibility, and platform readiness, enabling faster, more reliable experimentation and easier adoption of newer Python ecosystems. Highlights include opt-in progress visualization for hyperparameter optimization, a major DoE strategy model refactor with enhanced optimization capabilities, more forgiving candidate counting, platform/CI updates to support Python 3.10+ and modern dependencies, corrected desirability calculations for predictive strategies, and stability improvements through mapper tests.
January 2025
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for experimental-design/bofire: Delivered new capabilities to treat input features as output objectives and expanded support for categorical/discrete features, enabling more flexible optimization workflows and better alignment with real-world data.
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for experimental-design/bofire: Delivered new capabilities to treat input features as output objectives and expanded support for categorical/discrete features, enabling more flexible optimization workflows and better alignment with real-world data.
November 2024
8 Commits • 6 Features
Nov 1, 2024November 2024 monthly summary for experimental-design/bofire focused on expanding data handling, interoperability, and code quality to accelerate reliable experimentation and data-driven decisions. Delivered scalable data pipelines, enhanced model/domain validation, and targeted bug fixes that reduce risk in production and enable faster iteration.
8 Commits • 6 Features
Nov 1, 2024November 2024 monthly summary for experimental-design/bofire focused on expanding data handling, interoperability, and code quality to accelerate reliable experimentation and data-driven decisions. Delivered scalable data pipelines, enhanced model/domain validation, and targeted bug fixes that reduce risk in production and enable faster iteration.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024Month 2024-10: Delivered a new Piecewise Linear GP Surrogate for monotonic inputs in the bofire repo, enabling robust modeling of monotonic piecewise-linear functions via a Wasserstein kernel (with optional continuous kernel for additional features). Implemented data models, kernel mappings, and surrogate logic, backed by comprehensive tests to ensure reliability. Key commit referenced: Linear Interpolation (#443) with hash ef0ffc12036c7ee62f2a3ed4f590379c1b1cb128.
October 2024
1 Commits • 1 Features
Oct 1, 2024Month 2024-10: Delivered a new Piecewise Linear GP Surrogate for monotonic inputs in the bofire repo, enabling robust modeling of monotonic piecewise-linear functions via a Wasserstein kernel (with optional continuous kernel for additional features). Implemented data models, kernel mappings, and surrogate logic, backed by comprehensive tests to ensure reliability. Key commit referenced: Linear Interpolation (#443) with hash ef0ffc12036c7ee62f2a3ed4f590379c1b1cb128.
Activity
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Quality Metrics
Correctness89.4%
Maintainability87.8%
Architecture87.0%
Performance81.0%
AI Usage23.0%
Skills & Technologies
Programming Languages
C++JSONJupyter NotebookMarkdownPythonSQLTOMLUnknownYAML
Technical Skills
API CompatibilityAPI DesignAPI DevelopmentAPI developmentBackend DevelopmentBayesian OptimizationBoTorchBuild ConfigurationCI/CDCode CleanupCode DocumentationCode RefactoringConstraint HandlingConstraint ProgrammingContinuous Integration
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