Nicolas Hug developed high-performance image interpolation features for the ROCm/pytorch and pytorch/pytorch repositories, focusing on CPU-bound upsampling paths. He implemented a NEON-optimized interpolation for RGB images in channels-last format, achieving 3x-6x speedups while ensuring bitwise-equivalent outputs and antialiasing support. His work unified kernel dispatch logic, simplifying code paths and improving maintainability. Using C++ and NEON intrinsics, Nicolas introduced a 4-wide NEON optimization for F.interpolate, delivering further performance gains. Comprehensive testing and benchmarking validated correctness across multiple configurations. His contributions demonstrated deep expertise in CPU optimization, kernel development, and code refactoring, addressing both performance and code quality.