Gordon Watts
Gordon Watts is a professor of physics at the University of Washington, Seattle, and a member of the ATLAS experiment at the Large Hadron Collider at CERN and deputy director of the National Science Foundation's IRIS-HEP Software Institute. He has extensive lecture and tutorial teaching experience in classrooms, labs, and informal tutorial settings. One of his main ATLAS responsibilities is helping to bring python-based analysis techniques to the ~3000 physicists who are part of the ATLAS experiment.
Sessions
Despite its reputation for being slow, Python is the leading language of scientific computing, which generally needs large-scale (fast) computations. This is because most scientific problems can be split into "metadata bookkeeping" and "number crunching," where the latter is performed by array-oriented (vectorized) calls into precompiled routines.
This tutorial is an introduction to array-oriented programming. We'll focus on techniques that are equally useful in NumPy, Pandas, xarray, CuPy, Awkward Array, and other libraries, and we'll work in groups on three class projects: Conway's Game of Life, evaluating decision trees, and computations on ragged arrays.
GitHub repository: https://github.com/ekourlit/scipy2024-tutorial-thinking-in-arrays
The ATLAS experiment at CERN explores vast amounts of physics data to answer the most fundamental questions of the Universe. The prevalence of Python in scientific computing motivated ATLAS to adopt it for its data analysis workflows while enhancing users' experience. This talk will describe to a broad audience how a large scientific collaboration leverages the power of the Scientific Python ecosystem to tackle domain-specific challenges and advance our understanding of the Cosmos. Through a simplified example of the renowned Higgs boson discovery, attendees will gain insights into the utilization of Python libraries to discriminate a signal in immersive noise, through tasks such as data cleaning, feature engineering, statistical interpretation and visualization at scale.
GitHub repository for the talk: https://github.com/ekourlit/scipy2024-ATLAS-demo