experimental-design/bofire
Oct 2024 – Oct 2025
13 Months active
Languages Used
C++PythonJSONJupyter NotebookSQLTOMLYAMLMarkdown
Technical Skills
Bayesian OptimizationGPyTorchKernel MethodsMachine LearningPyTorchPython
Johannes P. Dürholt
PROFILE
Johannes P. Dürholt
Johannes Duerholt developed advanced optimization and experimental design features for the experimental-design/bofire repository, focusing on robust constraint handling, surrogate modeling, and scalable benchmarking. He engineered new constraint types, enhanced Bayesian optimization workflows, and refactored the optimization pipeline to support categorical, discrete, and mixed features. Using Python and PyTorch, Johannes implemented data models, kernel methods, and acquisition strategies that improved model interpretability and reliability. His work included compatibility updates for BoTorch, expanded test coverage, and integration of fully Bayesian surrogates. These contributions deepened the platform’s technical foundation, enabling reproducible, flexible experimentation and supporting reliable deployment in data-driven research environments.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
69Total
Bugs
11
Commits
69
Features
35
Lines of code
24,763
Activity Months13
Your Network
5 people
Work History
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.
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
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.
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.
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.
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
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.
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.
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.
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
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)).
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)).
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.
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
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
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
Activity
Loading activity data...
Quality Metrics
Correctness89.6%
Maintainability88.2%
Architecture87.0%
Performance79.6%
AI Usage20.4%
Skills & Technologies
Programming Languages
C++JSONJupyter NotebookMarkdownPythonSQLTOMLYAML
Technical Skills
API CompatibilityAPI DesignAPI DevelopmentBackend DevelopmentBayesian OptimizationBoTorchBuild ConfigurationCI/CDCode CleanupCode DocumentationCode RefactoringConstraint HandlingConstraint ProgrammingContribution GuidelinesData Manipulation
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline