secretflow/spu
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Algorithm ImplementationJAXLinear AlgebraNumerical ComputingScientific Computing
Bing Liu
PROFILE
Bing Liu
Developed a Jacobi rotation-based singular value decomposition (SVD) feature for symmetric matrices in the secretflow/spu repository, focusing on secure and privacy-preserving machine learning workflows. The implementation included core rotation logic, application of rotations, and an iterative diagonalization process to compute singular values and, optionally, singular vectors. Validation was performed against scikit-learn’s TruncatedSVD to ensure correctness and reliability. The work leveraged Python and JAX, drawing on expertise in linear algebra, numerical computing, and scientific computing. Code was aligned with repository standards and prepared for integration into downstream systems, enhancing matrix factorization capabilities for privacy-focused machine learning applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
332
Activity Months1
Your Network
38 peopleWork History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for secretflow/spu. Key feature delivered: Jacobi rotation-based SVD for symmetric matrices, with core rotation logic, application of rotations, and an iterative diagonalization process. It computes singular values and optionally singular vectors, and is validated against scikit-learn's TruncatedSVD. This enhancement strengthens matrix factorization capabilities for secure, privacy-preserving ML workflows and prepares the feature for downstream integration and production use.
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for secretflow/spu. Key feature delivered: Jacobi rotation-based SVD for symmetric matrices, with core rotation logic, application of rotations, and an iterative diagonalization process. It computes singular values and optionally singular vectors, and is validated against scikit-learn's TruncatedSVD. This enhancement strengthens matrix factorization capabilities for secure, privacy-preserving ML workflows and prepares the feature for downstream integration and production use.
April 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Algorithm ImplementationJAXLinear AlgebraNumerical ComputingScientific Computing
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