experimental-design/bofire
Dec 2025 – Feb 2026
3 Months active
Languages Used
Python
Technical Skills
Python programmingdata analysismolecular modelingunit testingData ModelingGaussian Processes
mustang24
PROFILE
Mustang24
Kislay Aravi developed advanced modeling features for the experimental-design/bofire repository, focusing on molecular descriptor handling, Gaussian Process kernel enhancements, and output transformation frameworks. Using Python and leveraging skills in data modeling and machine learning, Kislay introduced flexible descriptor filtering and 3D exclusion options to improve molecular feature selection. He implemented parameterized random sampling and new kernel methods, such as Index and spherical linear kernels, to support high-dimensional and categorical data. Additionally, Kislay built a modular output transformation system for ensemble surrogate models, centralizing validation logic. The work demonstrated depth in both algorithmic design and maintainable, test-driven engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
5,539
Activity Months3
Your Network
5 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — Experimental-design/bofire: Delivered the Output Transformation Framework for EnsembleMapSaasSingleTaskGPSurrogate, introducing Log and ChainedOutputTransform to enhance output scaling flexibility. Centralized validation for the Log transform moved into data_models to improve maintainability and error handling. These changes streamline experimentation with ensemble surrogate models, reduce configuration errors, and lay groundwork for future transform extensions. The work supports robust, scalable analytics pipelines and faster iteration cycles.
1 Commits • 1 Features
Feb 1, 2026February 2026 — Experimental-design/bofire: Delivered the Output Transformation Framework for EnsembleMapSaasSingleTaskGPSurrogate, introducing Log and ChainedOutputTransform to enhance output scaling flexibility. Centralized validation for the Log transform moved into data_models to improve maintainability and error handling. These changes streamline experimentation with ensemble surrogate models, reduce configuration errors, and lay groundwork for future transform extensions. The work supports robust, scalable analytics pipelines and faster iteration cycles.
February 2026
January 2026
3 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01) - Delivered two major feature areas in experimental-design/bofire, significantly expanding model flexibility and reliability. The work focused on parameterized random sampling and advanced Gaussian Process kernels (Index Kernel, Positive Index Kernel, and a spherical linear kernel) with supporting docs and tests. These changes improve usability for datasets with categorical features, boost modeling expressiveness for high-dimensional problems, and enhance maintainability through stronger typing and better test organization.
January 2026
3 Commits • 2 Features
Jan 1, 2026January 2026 (2026-01) - Delivered two major feature areas in experimental-design/bofire, significantly expanding model flexibility and reliability. The work focused on parameterized random sampling and advanced Gaussian Process kernels (Index Kernel, Positive Index Kernel, and a spherical linear kernel) with supporting docs and tests. These changes improve usability for datasets with categorical features, boost modeling expressiveness for high-dimensional problems, and enhance maintainability through stronger typing and better test organization.
December 2025
1 Commits • 1 Features
Dec 1, 2025Monthly work summary for 2025-12 focusing on experimental-design/bofire. Delivered Mordred Calculator enhancements including descriptor filtering to decorrelate features and an option to ignore 3D descriptors, along with lint fixes and type improvements for cleaner code. These changes improve usability, flexibility, and reliability of descriptor handling, enabling more robust experimentation with fewer runtime issues.
1 Commits • 1 Features
Dec 1, 2025Monthly work summary for 2025-12 focusing on experimental-design/bofire. Delivered Mordred Calculator enhancements including descriptor filtering to decorrelate features and an option to ignore 3D descriptors, along with lint fixes and type improvements for cleaner code. These changes improve usability, flexibility, and reliability of descriptor handling, enabling more robust experimentation with fewer runtime issues.
December 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage44.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ModelingGaussian ProcessesGaussian processesKernel MethodsMachine LearningPython programmingdata analysisdata modelingkernel methodsmachine learningmolecular modelingsampling methodssurrogate modelingunit testing
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