In March 2026, Youchen developed a fast GELU activation function for the ROCm/aiter repository, focusing on kernel-level optimizations and vectorized computation to enhance neural network throughput. Leveraging CUDA, C++, and Python, Youchen introduced new kernel definitions and applied performance optimization techniques to improve execution speed. Comprehensive unit tests and logging were integrated to ensure functionality, traceability, and maintainability, while minor bug fixes addressed issues in the test suite and import statements to stabilize the build process. The work demonstrated depth in deep learning and machine learning engineering, with careful attention to code quality, maintainability, and continuous integration reliability.