Rujiacai worked on the ROCm/aiter repository, focusing on improving the robustness of the quantization pipeline in machine learning workloads. Addressing a bug in the SmoothQuant HIP kernel, Rujiacai resolved an integer overflow issue by casting calculations to int64_t, ensuring numerical correctness beyond int32 limits. The update also included enhancements to the test suite, adding coverage for activation parameters to verify correct quantization behavior in the presence of activations. Utilizing C++, Python, and CUDA, Rujiacai’s work demonstrated careful attention to numerical reliability and production-readiness, delivering a targeted fix that deepened the reliability of quantization in production environments.