Iason Krommydas
I'm a PhD student in the Department of Physics and Astronomy at Rice University, conducting research in high-energy physics as a member of the CMS experiment at the Large Hadron Collider at CERN. My work focuses on studying Higgs boson decays into two photons, analyzing data collected by the CMS detector, and contributing to software development for large-scale scientific analyses. I'm passionate about scientific computing and open-source tools that enable reproducible and efficient research. I’m maintainer of Awkward Array, an array library for nested, variable-sized data, using NumPy-like idioms, and an author and maintainer of Coffea, a toolkit designed to simplify data analysis in particle physics. With deep experience in the scientific Python ecosystem, I enjoy building tools that drive insight and accelerate scientific discovery.
Sessions
Python has become the dominant language in scientific computing, even in domains that demand high performance. This is largely due to the power of array-oriented programming, which separates complex problems into two parts: lightweight bookkeeping and heavy numerical computation. The latter is handled efficiently by vectorized operations that rely on fast, precompiled libraries.
This tutorial introduces array-oriented programming as a distinct mindset that encourages new ways of structuring problems. Rather than focusing on any one library, we’ll cover general techniques that apply to any array library with a particular focus on NumPy and JAX. You'll work in groups on four class projects: Conway's Game of Life using arrays, iterative computations on arrays, just-in-time (JIT) compilation for the Mandelbrot set, and exploring data in ragged arrays. This tutorial focuses on the thought process: all of the problems are to be solved in an imperative way (for loops) and an array-oriented way.
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.