Keeley focused on enhancing autodiff robustness and backpropagation correctness in the tracel-ai/burn repository, addressing a critical crash in the scatter operation when operands differed in size. By refining gradient propagation and ensuring correctness across multiple autodiff operations, Keeley improved the reliability of tensor computations in Rust. The work included targeted fixes for gradient slicing to prevent misalignment during training, as well as improvements to backpropagation logic in operations like Cross.backward and CatStep. Through expanded test coverage and regression safeguards, Keeley’s contributions strengthened the backend’s autograd paths, supporting more dependable machine learning workflows and advancing the project’s core autodiff capabilities.