jax-ml/jax
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
JAXlinear algebranumpytesting
Jonathan Brodrick
PROFILE
Jonathan Brodrick
Over a two-month period, this developer enhanced core linear algebra capabilities in both the jax-ml/jax and scipy/scipy repositories. They implemented auto-batching for linear algebra functions in JAX, introducing deprecation warnings and expanding test coverage to support diverse input shapes and data types. In SciPy, they delivered a performance-focused update to the Cholesky decomposition routine by leveraging matrix symmetry and optimizing memory layout, accompanied by comprehensive benchmarks to validate accuracy across array layouts. Their work utilized Python, C++, and libraries such as JAX and NumPy, with a strong emphasis on numerical analysis, performance optimization, and robust testing practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
752
Activity Months2
Your Network
503 peopleSame Organization
@simulation.science5
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Delivered a performance-focused enhancement for Cholesky decomposition in SciPy by exploiting matrix symmetry and memory layout optimizations. Added benchmarks and validation to ensure accuracy across contiguous and non-contiguous array layouts. The update improves practical performance of linear algebra routines in SciPy, benefiting users with large-scale numerical workloads.
1 Commits • 1 Features
Apr 1, 2026April 2026: Delivered a performance-focused enhancement for Cholesky decomposition in SciPy by exploiting matrix symmetry and memory layout optimizations. Added benchmarks and validation to ensure accuracy across contiguous and non-contiguous array layouts. The update improves practical performance of linear algebra routines in SciPy, benefiting users with large-scale numerical workloads.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (jax-ml/jax): Delivered auto-batching for linear algebra functions across the JAX linear algebra module, enabling scalable batched computations. Introduced deprecation warnings for batched operations to guide migration to the new auto-batching API, and expanded test coverage across diverse input shapes and data types to ensure correctness. Commit reference: 7d4fae027ba75ddc46b5a42e385eee91066f7680 (support scipy-style auto-batching jsp.linalg).
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 (jax-ml/jax): Delivered auto-batching for linear algebra functions across the JAX linear algebra module, enabling scalable batched computations. Introduced deprecation warnings for batched operations to guide migration to the new auto-batching API, and expanded test coverage across diverse input shapes and data types to ensure correctness. Commit reference: 7d4fae027ba75ddc46b5a42e385eee91066f7680 (support scipy-style auto-batching jsp.linalg).
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
JAXlinear algebranumerical analysisnumpyperformance optimizationtesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
scipy/scipy
Apr 2026 – Apr 2026
1 Month active
Languages Used
C++Python
Technical Skills
linear algebranumerical analysisperformance optimizationtesting