Worked on the google-research/swirl-dynamics repository to enhance the evaluation metrics pipeline by introducing support for nested dictionaries and improving result logging. This feature expanded the schema for evaluation metrics, allowing for more complex and detailed analytics during model assessment. The technical approach involved Python-based processing of metrics and robust logging patterns, which increased traceability and facilitated faster debugging. By reinforcing the maintainability and scalability of the evaluation workflow, the changes enabled clearer experiment outcomes and more reliable model evaluation. The work demonstrated skills in data analysis, machine learning, and Python, laying groundwork for richer and more scalable experiment reporting.