In March 2026, Elmanj17 developed a performance-optimized Dense Matrix Multiply Kernel for the apache/systemds repository, focusing on efficient handling of transposed matrix inputs. By implementing specialized kernels in Java for common patterns such as t(A)%*%B and A%*%t(B), Elmanj17 eliminated the need for explicit transpose operations, instead enabling in-place or tiled-transposition. This approach reduced both runtime and memory allocations for dense matrix multiplication, particularly benefiting analytics workloads involving 100x100 matrices. The work demonstrated strong skills in matrix operations and performance optimization, and was validated through regression tests and performance suites to ensure correctness and measurable efficiency gains.