Henry Schreiner
Henry Schreiner is a Computational Physicist / Research Software Engineer in High Energy Physics at Princeton University. He specializes in the interface between high-performance compiled codes and interactive computation in Python, in software distribution, and in interface design. He has previously worked on computational cosmic-ray tomography for archaeology and high performance GPU model fitting. He is currently a member of the IRIS-HEP project, developing tools for the next era of the Large Hadron Collider (LHC).
He is a maintainer/core developer for packaging, build, scikit-build, cibuildwheel, pybind11, meson-python, nox, and plumbum for Python. He is an admin of Scikit-HEP, and a lead designer on boost-histogram, hist, UHI, vector, uproot-browser, Particle, and DecayLanguage packages there. He is also the lead author of the Scientific-Python Development guide and Scientific-Python/cookie. He is the primary author of CLI11, a C++ library used by Microsoft terminal and many others. He is also the lead web developer for IRIS-HEP. He is also the author of Modern CMake and a variety of CMake, GPU, and Python training courses and classes.
Sessions
At CERN's Large Hadron Collider, we collide protons at near light-speed to discover new particles and understand fundamental physics. Python is becoming the primary language for analyzing this data, marking a significant evolution from the Fortran and C++ workflows of previous decades.
This talk explores the modern Python-based analysis pipeline of High-Energy Physics (HEP) and the technical challenges it addresses. We'll present how we handle nested, jagged data structures and work with data at the petabyte scale using the community-driven Scikit-HEP ecosystem of specialized tools for efficient and high-performance data analysis.
We'll show how we're building a Python stack that integrates with distributed computing frameworks and leverages GPU acceleration. Beyond domain-specific analysis tools, HEP's transition to Python has driven improvements to the broader Python packaging ecosystem, including contributions to cibuildwheel, the development of scikit-build-core, and advances in pybind11, benefiting anyone building Python packages with compiled extensions.
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.
Google Form for questions for the panel: https://forms.gle/1YP4951Yb9U4r2md6