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Mehdi Ataei

PROFILE

Mehdi Ataei

Mehdi Ataei developed automatic differentiation support for Warp kernels within the NVIDIA/warp repository, enabling seamless integration with JAX for machine learning and scientific computing workflows. He implemented adjoint computation in the jax_kernel module, allowing Warp kernels to be differentiable and thus suitable for gradient-based optimization. Using C++ and Python, Mehdi leveraged skills in CUDA, FFI, and kernel development to deliver this feature, along with comprehensive documentation and unit tests to ensure reliability and ease of adoption. His work addressed integration friction between Warp and JAX, laying a technical foundation for more flexible and efficient experimentation in differentiable programming.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

1Total
Bugs
0
Commits
1
Features
1
Lines of code
924
Activity Months1

Work History

October 2025

1 Commits • 1 Features

Oct 1, 2025

October 2025 Monthly Summary for NVIDIA/warp: Delivered JAX Warp Kernel Automatic Differentiation feature with adjoint computation, plus docs and tests. This enables differentiable Warp kernels within JAX for ML and scientific computing workflows, reducing integration friction and accelerating experimentation.

Activity

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Quality Metrics

Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++Python

Technical Skills

Automatic DifferentiationCUDAFFI (Foreign Function Interface)JAXKernel Development

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

NVIDIA/warp

Oct 2025 Oct 2025
1 Month active

Languages Used

C++Python

Technical Skills

Automatic DifferentiationCUDAFFI (Foreign Function Interface)JAXKernel Development

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