experimental-design/bofire
Feb 2026 – Mar 2026
2 Months active
Languages Used
Python
Technical Skills
Pythondata analysismachine learningoptimizationscientific computing
Kateryna Morozovska
PROFILE
Kateryna Morozovska
Contributed to the experimental-design/bofire repository by enhancing the reliability and reproducibility of data analysis workflows. Focused on preserving original DataFrame indices during NonlinearConstraint evaluation, ensuring data integrity and alignment for downstream analyses. Improved the BoFire evaluation engine by enforcing Python backend usage for Jacobian and Hessian calculations involving list expressions, which increased computational accuracy. Updated documentation to clarify index handling and provide better guidance for reaction optimization, supporting maintainability and onboarding. Demonstrated proficiency in Python, data analysis, and scientific computing, with attention to repository hygiene and collaborative development practices that stabilized pipelines and reduced debugging time for users.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
693
Activity Months2
Your Network
5 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 20262026-03 Monthly Summary: Strengthened the BoFire evaluation workflow for Jacobian/Hessian calculations involving list expressions and improved user guidance for reaction optimization. Delivered reliable Python backend evaluation, stabilized engine behavior, and documentation updates to support maintainability and onboarding.
1 Commits • 1 Features
Mar 1, 20262026-03 Monthly Summary: Strengthened the BoFire evaluation workflow for Jacobian/Hessian calculations involving list expressions and improved user guidance for reaction optimization. Delivered reliable Python backend evaluation, stabilized engine behavior, and documentation updates to support maintainability and onboarding.
March 2026
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for the experimental-design team focusing on delivering reliable data alignment, maintaining data integrity, and enabling reproducible downstream analyses. Repository: experimental-design/bofire.
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for the experimental-design team focusing on delivering reliable data alignment, maintaining data integrity, and enabling reproducible downstream analyses. Repository: experimental-design/bofire.
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Pythondata analysismachine learningoptimizationscientific computing
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