Nicolas Korjahn developed and integrated the Shampoo Optimizer for neural network training within the apache/systemds repository, focusing on both full-matrix and diagonal preconditioning techniques. His work included implementing momentum updates and a heuristic variant that delays preconditioner updates and reduces the frequency of root recomputation, aiming to improve convergence and training efficiency. Using DML and Python, Nicolas extended existing neural network training scripts and introduced comprehensive tests to validate correctness and convergence. The feature was committed as an initial prototype and accompanied by staging experiments, demonstrating a deep technical approach to optimization algorithms and machine learning workflows in SystemDS.