Charlie Fu developed multi-dimensional input support for TunedGemm in the ROCm/aiter repository, enabling batched GEMM operations on tensors with three or more dimensions. He implemented robust reshaping and validation logic in Python to ensure correct matrix dimensions across varying batch shapes, addressing reliability and error handling for deep learning and machine learning workloads. This work expanded the flexibility of TunedGemm, reducing the need for manual input adjustments and supporting broader model scalability. By focusing on performance optimization and input correctness, Charlie laid a solid foundation for future enhancements, demonstrating depth in both technical implementation and understanding of workload requirements.