2026-07-20 –, Room 1.38 (Ground Floor, Turing)
In Fall 2025, the UConn School of Mechanical, Aerospace, and Manufacturing Engineering launched Open Source Experiences, an elective course developed in partnership with six NumFOCUS-supported projects (napari, BiocPy, Blosc, MNE-Python, mlpack, JuliaHub). The course embedded students directly into active open source communities, where they contributed to the codebases, collaborated with project maintainers, and learned about community-driven open source software development. In this talk, we will share the lessons learned from piloting this collaboration model, and how these experiences benefit students, open source and open science communities, and educators alike. Attendees will take away actionable insights for integrating open source contributions into their own classrooms and programs.
The scientific open source community thrives on shared knowledge and welcoming communities, the very system of values that the annual EuroSciPy conference celebrates. At the same time, educators in computational sciences and engineering seek ways to help students move beyond traditional assignments and into experiential learning. Where experiential is a combination of skill-building, networking, and understanding of how science and software happen in the real world. Open Source Experiences was designed to meet both of these needs.
In this talk, we will share how we worked with students and open source community mentors to structure a semester-long course where students made valuable contributions to existing scientific Python projects. Students participated in issue triage, bug fixes, documentation improvements, and feature contributions, guided by project maintainers. Through this format, students gained experience with tooling (version control, CI/CD, code formatting, testing), in community practices (contributing guidelines, communication norms), and long-term project planning (design decisions, roadmap alignment), while participating projects gained valuable contributions and new contributors.
Student participation and contributions were assessed with regular progress updates. As instructors, we facilitated discussions to guide the Open Source Experience learning process: working on bugs and issues in an open environment, community expectations, GitHub best practices, etc.
Talk outline:
- Course design and goals: balancing academic learning objectives with community needs, assessment strategies.
- Collaboration with maintainers: selecting projects, preparing onboarding documentation, setting expectations, and creating a mentorship model that respects both students’ learning and maintainer time.
- Student outcomes: reflections on learning gains around technical skills, professional communication, and confidence engaging in open source ecosystems.
- Challenges and lessons learned.
We’ll also share examples of student contributions and how they augmented both the ecosystem and the students’ portfolios.
Whether you’re an educator thinking about how to bring open source into your curriculum or a project leader looking for ways to engage with academic institutions to widen your project’s contributor pipeline, this talk will give you concrete ideas to adapt.
Inessa is building bridges between people, open source software, and open science. Over the years, she has launched and continues to support several educational initiatives focused on widening the open source contributor pipeline. Inessa is Open Source Program Manager at OpenTeams and guest faculty at University of Connecticut. She also serves on the NumPy Steering Council and the pyOpenSci Advisory Board. Inessa is perpetually fascinated by incentive design, collaborative intelligence, and jazz.
Ryan C. Cooper is an Associate Professor-in-Residence at the University of Connecticut. His background is in mechanics and materials science with an emphasis on numerical simulations and engineering education. He has been using Jupyter and GitHub to enhance the classroom experience for over six years. Prof. Cooper has developed and free open source materials for computational work in engineering and volunteered with the NumPy documentation team SciPy track chair. Ryan is an integral part of the AI in the School of Engineering committee. He has a Ph.D. from Columbia University and spent two and a half years at Oak Ridge National Laboratory as a Postdoctoral researcher.
Dr. Ryan Curtin is an independent researcher and open-source software developer, leading the development and maintenance of several packages in the C++ scientific software ecosystem. During his Ph.D. at Georgia Tech he focused on the formalization of dual-tree algorithms, a class of geometric branch-and-bound algorithms that can be used to solve subproblems relevant to machine learning techniques. These algorithms underlie the efficient mlpack C++ machine learning library, which he has
led since 2010. In his free time, he races go-karts, so he never escapes from trying to go fast in one way or another.