Ivan Ogasawara is a versatile software engineer and teaching assistant with over two decades of development experience and a strong focus on compilers, data science, and open science. He blends hands-on backend expertise in Python, C++, and SQL with front-end and DevOps skills, having architected systems, led technical teams, and packaged software for reproducible research. A prolific open-source contributor, Ivan has improved core Jupyter functionality and expanded Ibis’ OmniSciDB backend with geospatial and date/time features, demonstrating impact on widely used scientific tooling. His work spans research software engineering in transportation and healthcare to production services, and he helps communities as a pyOpenSci reviewer and Open Science Labs leader. Notably, beyond coding, he has organized compiler study groups and DEI initiatives, showing a commitment to technical learning and inclusive collaboration. Based in Texas and Brazil, he currently balances teaching roles with leading technical efforts at LiteRev.
11 years of coding experience
12 years of employment as a software developer
Engenharia de Transportes e Gestão Territorial, Transportation Engineering, Master Degree (interrupted), Engenharia de Transportes e Gestão Territorial, Transportation Engineering, Master Degree (interrupted) at Universidade Federal de Santa Catarina
Information System Specialization, Information Technology, 7,5, Information System Specialization, Information Technology, 7,5 at Centro Universitário Eniac
Contributions:22 reviews, 75 commits, 122 PRs in 3 years 3 months
Contributions summary:Ivan primarily contributed to the development of the MapD (OmniSciDB) backend within the Ibis project, implementing features such as the `Where` operator, `CrossJoin` operator, and handling joining with different column names. They also added support for geospatial functions and data type casts, including `float32` and geospatial types for creating tables from schemas. Furthermore, the user worked on improving the OmniSciDB client, fixing issues with the schema and adding methods like `sql`. The user also addressed bugs related to the extraction of various date and time components.
Contributions:5 commits, 1 PR, 18 comments in 2 months
Contributions summary:Ivan primarily focused on refactoring and improving the `jupyter_core/command.py` file. Their work involved removing potentially problematic environment variable modifications, correcting import statements, and updating the `_jupyter_abspath` function to use a more direct approach. The user also improved the code's structure and readability by refining the `main` function and docstrings. These modifications contribute to the core functionality and maintainability of the Jupyter command-line interface.
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