Simon Blanke focused on enhancing the reproducibility and stability of proximity-based estimators in the sktime/sktime repository. He addressed a critical bug in the Proximity Forest and Proximity Stump algorithms by implementing deterministic random state management, pre-generating random states for all trees to ensure consistent results. Using Python, Simon improved the pickling process by restructuring distance computations to reuse random number generators and instantiate new transformers, resolving closure-related serialization issues. His work leveraged skills in algorithm implementation, parallel processing, and machine learning, resulting in more reliable experiments and deployments. The depth of these changes improved both reproducibility and deployment safety.