Developed a sparse input optimization for the LeverageScore.default function in the satijalab/seurat repository, focusing on enhancing scalability for large datasets. The solution introduced logic to convert IterableMatrix inputs to a sparse format when the number of cells is less than 1.5 times the number of sketches, thereby reducing memory usage and improving preprocessing speed. This targeted approach was implemented using R and leveraged skills in data analysis and optimization to address performance bottlenecks in workflows handling large matrices. The work improved efficiency for specific data structures without affecting broader functionality, reflecting a precise and context-aware engineering contribution.