Jyoti Aneja developed and integrated the MMLU benchmark and a baseline experiment pipeline into the microsoft/eureka-ml-insights repository, focusing on enabling comprehensive model evaluation across diverse subjects. Using Python, she implemented reusable data processing utilities that streamline the preparation and handling of the MMLU dataset, supporting reproducible machine learning experiments. Her work included defining a baseline configuration for running MMLU experiments end-to-end, which facilitates consistent benchmarking and model comparison within the repository. Over the course of the month, Jyoti’s contributions demonstrated depth in benchmark implementation and data processing, providing a robust foundation for future machine learning evaluation and research efforts.