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 experience in the scientific Python ecosystem, I enjoy building tools that drive insight and accelerate scientific discovery.
Session
Python has become the dominant language for scientific computing and data science, largely due to powerful array libraries that enable high-performance numerical computation. This tutorial introduces array-oriented programming as a paradigm and surveys the modern Python array ecosystem.
We'll explore when and how to use different array libraries: NumPy for general-purpose array operations, JAX for automatic differentiation, just-in-time compilation of array-oriented code, and GPU acceleration, Numba for just-in-time compilation of imperative code, and Awkward Array for nested and irregular data structures. Through live demos, we'll show how to think in arrays, discuss the limitations of array-oriented programming, and demonstrate how JIT compilation addresses these challenges.
Whether you're analyzing data, building machine learning models, or doing scientific simulations, understanding the strengths and trade-offs of each library will help you choose the right tool for your problem.