William Gulian developed a memory optimization for the to_pandas() function in the mathworks/arrow repository, focusing on efficient extension column handling to support large-scale data processing. He refactored the PandasOptions storage in C++ to use shared pointers and unordered sets, which reduced memory usage from approximately 7GB to 192MB without altering the public Python API. His work demonstrated strong skills in C++ development, data structures, and memory management, with thorough testing and profiling to ensure stability. By collaborating across teams and maintaining API compatibility, William delivered a robust internal improvement that enhances the scalability of Python data workflows.