Aaron Kaw focused on enhancing the symbolic differentiation engine in the JuliaSymbolics/Symbolics.jl repository by developing comprehensive consistency tests. He implemented new unit tests to verify that derivative calculations yield consistent results when functions are applied directly or through the BasicSymbolic representation. This approach strengthened the reliability and maintainability of the codebase by expanding test coverage and introducing early regression detection mechanisms. Working primarily in Julia and leveraging skills in software development, symbolic computation, and unit testing, Aaron’s contributions addressed foundational correctness rather than bug fixes, reflecting a methodical effort to improve the long-term stability and accuracy of symbolic computation workflows.