Matthew Feickert
Matthew is a research scientist in experimental high energy physics and data science at the University of Wisconsin-Madison Data Science Institute (a “data physicist”). He works as a member of the ATLAS collaboration on searches for physics beyond the standard model with experiments performed at CERN's Large Hadron Collider (LHC) in Geneva, Switzerland. He also serves on the executive board of the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) where he is a researcher and the Analysis Systems Area lead. He is also a topical editor for physics and data science for the Journal of Open Source Software. He previously did his Ph.D. (2019) research at Southern Methodist University, also on the ATLAS experiment, and was a postdoc at the University of Illinois at Urbana-Champaign, and the University of Wisconsin-Madison.
Sessions
Scientific researchers need reproducible software environments for complex applications that can run across heterogeneous computing platforms. Modern open source tools, like Pixi, provide automatic reproducibility solutions for all dependencies while providing a high level interface well suited for researchers.
This tutorial will provide a practical introduction to using Pixi to easily create scientific and AI/ML environments that benefit from hardware acceleration, across multiple machines and platforms. The focus will be on CUDA applications, such as machine learning frameworks and use of CUDA Tile, as well as using pixi-build to construct bespoke CUDA enabled conda packages.
Installation Instructions: https://matthewfeickert-talks.github.io/reproducible-cuda-workflows-with-pixi-scipy-2026/setup/
Supply chain attacks on Python, including recent compromises of popular packages and CI workflows, have exposed structural weaknesses in the scientific Python ecosystem. This BoF will bring together library maintainers, downstream users, and security practitioners to discuss practical strategies for securing scientific Python stacks, from core packages (NumPy/SciPy) to domain libraries and analysis workflows. We will share current efforts (e.g., SPEC 8, Trusted Publishing, SBOM generation, GitHub Actions hardening), identify pain points and gaps, and brainstorm actionable steps the community can take over the next year to make scientific Python releases more trustworthy by default. Join us to share your experiences, challenges, and ideas on fortifying our open-source projects against potential threats and ensuring the integrity of scientific research.
Until very recently, producing and using reproducible scientific software environments required advanced knowledge and a strict adherence to best practices (e.g. DOI: 10.25080/majora-212e5952-028). Now, with the advent of modern tooling with lockfile-first workflows (i.e. Pixi and uv), and the emergence of lockfile standards across scientific open source, applications can be made reproducible at the digest level through tooling decisions. As this technology and practices become increasingly common there is an opportunity to define common best practices around lockfile based software development that can further reduce developer overhead and maintenance burden. This Birds of a Feather panel will focus on how experienced developers are leveraging lockfiles across software development, applications, and deployment while providing best practices and practical recommendations, while also highlighting continuing challenges and opportunities for improvement.