apache/systemds
Dec 2025 – Dec 2025
1 Month active
Languages Used
DMLJavaPythonR
Technical Skills
Java programmingPython programmingR programmingdata preprocessingmatrix operationsstatistical analysis
KilianBat
PROFILE
Kilianbat
During December 2025, Kilian Batliner developed a robust data scaling function for the apache/systemds repository, focusing on outlier-resistant preprocessing. He implemented a built-in feature that scales input matrices using the median and interquartile range, reducing sensitivity to outliers and supporting more reliable downstream statistical analysis. This work, aligned with SYSTEMDS-3863, enhanced the consistency of data science workflows by improving preprocessing reliability. Kilian utilized Java and R, applying skills in matrix operations and statistical analysis to ensure maintainability and integration within the codebase. The feature addressed a key need for dependable preprocessing pipelines, contributing depth to the project’s analytics capabilities.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
299
Activity Months1
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Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for the apache/systemds repo: Delivered Robust Data Scaling (Median + IQR) as a built-in function to enable outlier-resistant preprocessing. The new robust scaling uses median and IQR to scale input matrices, reducing sensitivity to outliers and improving reliability of downstream statistical analyses. This work supports SYSTEMDS-3863 and closes #2278. No major bugs documented for the period; focus was on delivering a robust feature and ensuring maintainability. Impact includes more dependable preprocessing pipelines, enabling more consistent modeling and analytics across datasets. Demonstrated skills in robust statistics, feature integration, and code quality within SystemDS.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for the apache/systemds repo: Delivered Robust Data Scaling (Median + IQR) as a built-in function to enable outlier-resistant preprocessing. The new robust scaling uses median and IQR to scale input matrices, reducing sensitivity to outliers and improving reliability of downstream statistical analyses. This work supports SYSTEMDS-3863 and closes #2278. No major bugs documented for the period; focus was on delivering a robust feature and ensuring maintainability. Impact includes more dependable preprocessing pipelines, enabling more consistent modeling and analytics across datasets. Demonstrated skills in robust statistics, feature integration, and code quality within SystemDS.
December 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DMLJavaPythonR
Technical Skills
Java programmingPython programmingR programmingdata preprocessingmatrix operationsstatistical analysis
Repositories Contributed To
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