About My Research
In my research at Sandia National Laboratories, I study how people, practices, and tools come together to create trustworthy, high-quality software for science and engineering applications. Today, computing is central to the practice of science, and research software acts as a powerful engine of discovery and innovation that advances the human condition. Despite the importance of this class of software, however, the needs of research software developers are understudied relative to those in mainstream industry. The central goal of my work is to develop software engineering approaches that are tailored to the realities of scientific research.
In order to create solutions that fit with how people work and think, we must begin with understanding them. For that reason, I investigate how developers coordinate their expertise, divide responsibility, mentor one another, make quality trade-offs, and maintain shared understanding in software systems that are often too large, complex, and multidisciplinary for any one person to fully comprehend. I use empirical methods — including interviews, surveys,in-depth case studies, and repository mining — to understand their needs and develop practical interventions.
What those interventions look like can vary, from process improvement toolkits, published guidelines, and training workshops to program analysis tools and community platforms. Across these different forms, the aim is the same: to expand what scientific teams can accomplish while keeping the understanding, judgment, and accountability on which trustworthy research front and center.
Reed Milewicz, Connor Brynteson, Ella Luedeke, and Italo Santos. (2026). We Create Quality: Towards a Human-Centric Theory of Research Software Quality in the Age of AI. 4th Annual United States Research Software Engineering Conference (US-RSE'26).
The paper argues that research software quality remains fundamentally human in the age of AI because people create and sustain it through their values, expertise, and collaboration, even as AI accelerates software production. Recently Accepted, Details Forthcoming ↗
Hana Frluckaj, Benjamin H. Sims, Reed M. Milewicz, Elaine M. Raybourn, David M. Rogers, Killian Muollo, Miranda Mundt, and Will Sutherland. (2026). Shadow of the Future: Developing Trust and Software within the Exascale Computing Project. Future Generation Computer Systems.
An interview study of Exascale Computing Project teams examining how expectations of continued collaboration fostered trust, sustained cross-project relationships, and improved scientific software quality. Paper ↗
Zixuan Feng, Reed Milewicz, Emerson Murphy-Hill, Tyler Menezes, Alexander Serebrenik, Igor Steinmacher, and Anita Sarma. (2026). Charting Uncertain Waters: A Socio-Technical Roadmap for Sustaining Open Source Communities in the Age of GenAI. ACM Transactions on Software Engineering and Methodology.
A socio-technical roadmap examining how generative AI may reshape open-source software practices, documentation, community engagement, and governance, and how communities can respond proactively to sustain their long-term resilience. Paper ↗
Addi Malviya Thakur, Reed Milewicz, Mahmoud Jahanshahi, Lavínia Paganini, Bogdan Vasilescu, and Audris Mockus. (2025). Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software. ACM International Conference on the Foundations of Software Engineering.
A large-scale study of more than 18,000 scientific software projects showing that scientific open-source software tends to remain active longer than comparable non-scientific projects and identifying factors associated with software longevity. Paper ↗