During February 2025, Seokhwan Kim focused on enhancing the correctness and robustness of batched matrix-vector operations in the JuliaGPU/CUDA.jl repository. He addressed a bug affecting batched GEMV computations, particularly for transposed matrices and varying batching scenarios. By introducing comprehensive tests and enforcing consistent input dimensions, Seokhwan ensured that the CUDA.jl gemv function handled edge cases reliably and prevented dimensionality errors. His work leveraged Julia and CUDA, applying expertise in GPU computing and linear algebra to improve the reliability of batched operations. The depth of his contributions strengthened the foundation for accurate and robust GPU-accelerated linear algebra workflows.