In July 2025, Mqh contributed to the pytorch/pytorch repository by implementing MTIA device support for foreach and fused kernels. This work involved expanding PyTorch’s device compatibility, allowing MTIA hardware to execute foreach and fused kernel operations, which can improve performance and efficiency for MTIA-based machine learning workloads. Mqh’s approach required a deep understanding of PyTorch’s backend device subsystems and kernel dispatch mechanisms, utilizing Python and backend development skills. The feature laid the foundation for hardware-accelerated pipelines on MTIA, addressing compatibility gaps and enabling new optimization pathways for users leveraging machine learning workflows on this emerging device platform.