Over two months, Chourasiya contributed to google-deepmind/torax and zio/zio-schema, focusing on backend and data processing challenges. In torax, he enhanced transport modeling by exposing Bohm and GyroBohm contributions, refining post-processing outputs, and expanding test coverage, using Python, Pydantic, and scientific computing techniques to improve analysis stability and developer onboarding. For zio-schema, he strengthened Avro decoding by enabling support for single-field GenericRecords and adding targeted tests, leveraging Scala and schema encoding skills to increase pipeline reliability. Chourasiya’s work demonstrated depth in code refactoring, documentation, and robust testing, addressing nuanced requirements in scientific and data-driven systems.