Over a two-month period, contributed to numpy/numpy by developing an optimized einsum implementation with batched matrix multiplication support, enhancing performance for high-dimensional tensor operations and improving the robustness of einsum equations. Applied Python and numpy expertise to refine einsum parsing, address edge cases like unique indices and broadcasting, and expand test coverage for reliability. Additionally, created the initial symmray recipe in conda-forge/staged-recipes, establishing package metadata, dynamic Python versioning, and dependency management with cotengra integration. Focused on algorithm optimization, numerical computing, and packaging, the work improved reproducibility and laid the foundation for broader ecosystem adoption and workflow efficiency.