secretflow/spu
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Algorithm ImplementationJAXLinear AlgebraNumerical ComputingScientific Computing
Bing Liu
PROFILE
Bing Liu
During April 2025, Bing Liu developed a Jacobi rotation-based singular value decomposition (SVD) feature for symmetric matrices in the secretflow/spu repository. Leveraging Python and JAX, Bing implemented the core rotation logic, applied iterative diagonalization, and enabled computation of both singular values and optional singular vectors. The solution was validated against scikit-learn’s TruncatedSVD to ensure correctness and reliability. Bing’s work focused on algorithm implementation and linear algebra, aligning the codebase with repository standards and preparing it for integration into privacy-preserving machine learning workflows. This contribution deepened the matrix factorization capabilities required for secure, production-ready scientific computing 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
36 people
Work 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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline