During April 2025, Mathanraj focused on enhancing the correctness and reliability of XGBoost MOJO pipelines in the h2oai/h2o-3 repository. He addressed a nuanced bug in Java that affected prediction accuracy when handling zero offsets, updating the score0 method to apply offsets conditionally based on model training parameters. To ensure robust validation, he expanded the test suite in XGBoostJavaMojoModelTest.java and introduced new MOJO test data, covering scenarios with both zero and non-zero offsets. This work improved the dependability of model deployment and scoring, demonstrating depth in Java development, machine learning integration, and comprehensive testing practices within production environments.