Welcome and Overview

• Peter Elmer (Princeton University (US))

Room: 407 Jadwin Hall

Collaborative Software Development with Git(Hub)

• David Lange (Princeton University (US))

Room: 407 Jadwin Hall Git is perhaps the single biggest denominator among all software developers, regardless of field or programming language. It serves not only as a version control system but as the backbone of all collaborative software development. This session aims to be 100% hands-on and at least 90% collaborative. We will exclusively work in the browser, using GitHub and GitHub codespaces. Learn forking, branching, opening pull requests, handling merge requests, and more. GitHub account required.

Getting connected to our compute platform

• David Lange (Princeton University (US))
• Peter Elmer (Princeton University (US))

Room: 407 Jadwin Hall

The Scientific Python Ecosystem

• Ianna Osborne (Princeton University)
• Andres Rios-Tascon (Princeton University)

Room: 407 Jadwin Hall Python has become the dominant programming language for scientific computing, combining an accessible high-level interface with high-performance compiled libraries. Although Python itself is interpreted, much of the scientific ecosystem is built on optimized C, C++, and CUDA implementations, enabling researchers to write expressive code while achieving near-native performance. This tutorial introduces the modern Scientific Python ecosystem for data analysis and high-performance computing. We begin with the fundamentals of NumPy, SciPy, Matplotlib, and JIT compilation using Numba, before exploring GPU programming with CuPy and `cuda.compute`, including parallel algorithms, iterators, and GPU execution patterns. The second half of the tutorial focuses on the Scikit-HEP ecosystem for high-energy physics. Participants will learn how to read ROOT data with Uproot, analyze irregular event data using Awkward Array, perform jet clustering with FastJet, fit models with iminuit, and carry out statistical inference with pyhf. Throughout the tutorial, we emphasize vectorized programming, composable array operations, and scalable analysis workflows that run efficiently on both CPUs and GPUs.

What Every Computational Physicist Should Know About Computer Architecture

• David Lange (Princeton University (US))

Room: 407 Jadwin Hall These days, everyone in physics in a computational physicist in one way or another. Experiments, theory, and (obviously) simulations all rely heavily on computers. Isn't it time you got to know them better? Computer architecture is an interesting study in its own right, and how well one understands and uses the capabilities of today's systems can have real implications for how fast your computational work gets done. Let's dig in, learn some terminology, and find out what's in there.

The Scientific Python Ecosystem

• Andres Rios-Tascon (Princeton University)
• Ianna Osborne (Princeton University)

Room: 407 Jadwin Hall Python has become the dominant programming language for scientific computing, combining an accessible high-level interface with high-performance compiled libraries. Although Python itself is interpreted, much of the scientific ecosystem is built on optimized C, C++, and CUDA implementations, enabling researchers to write expressive code while achieving near-native performance. This tutorial introduces the modern Scientific Python ecosystem for data analysis and high-performance computing. We begin with the fundamentals of NumPy, SciPy, Matplotlib, and JIT compilation using Numba, before exploring GPU programming with CuPy and `cuda.compute`, including parallel algorithms, iterators, and GPU execution patterns. The second half of the tutorial focuses on the Scikit-HEP ecosystem for high-energy physics. Participants will learn how to read ROOT data with Uproot, analyze irregular event data using Awkward Array, perform jet clustering with FastJet, fit models with iminuit, and carry out statistical inference with pyhf. Throughout the tutorial, we emphasize vectorized programming, composable array operations, and scalable analysis workflows that run efficiently on both CPUs and GPUs.

The Scientific Python Ecosystem

• Andres Rios-Tascon (Princeton University)
• Ianna Osborne (Princeton University)

Room: 407 Jadwin Hall Continued from the previous session, Part 2 focuses on the HEP software ecosystem.

An Introduction to Parallel Programming with OpenMP

• Tim Mattson (Intel-Retired)

Room: 407 Jadwin Hall We introduce parallel systems and the fundamental concepts needed to write parallel software.  As much as possible, we cover these concepts by writing OpenMP code.  By the time we’re done, you’ll understanding the key ideas behind parallel programming in general, but you’ll also have a deep understanding of the most commonly used elements of OpenMP.   We’ll start with how to manipulate threads directly for shared address spaces systems (such as multicore CPUs).

OpenMP and parallel programming beyond shared memory CPUs

• Tim Mattson (Intel-Retired)

Room: 407 Jadwin Hall We continue with OpenMP by exploring: (1) how data is managed in shared memory systems and (2) task level parallelism. We’ll finish our journey into parallel programming with a high-level discussion of how the concepts we’ve learned with OpenMP map onto GPU programming and programming distributed memory systems using MPI.  The goal is a solid working knowledge of OpenMP and a high level understanding of parallel programming beyond shared memory, CPU-based systems.

Introduction to Machine Learning

• Liv Helen Vage (Princeton University (US))
• Michelle Kuchera (Davidson College)

Room: 407 Jadwin Hall In this session we cover the basics of machine learning. We look at gradient descent and a few simple ML models including decision trees. Whether you're a complete beginner or have done a lot of ML, there will be something for everyone.

Machine learning - Neural networks & Kaggle

• Michelle Kuchera (Davidson College)
• Liv Helen Vage (Princeton University (US))

Room: 407 Jadwin Hall Almost all advanced ML methods use neural networks. We look at why that is and how they work. We also introduce a Kaggle competition which you will work on during the week. This will let you get experience in a real world ML problem.

Floating Point Arithmetic Is Not Real

• Tim Mattson (Intel)

Room: 407 Jadwin Hall

The Use and Abuse of Random Numbers

• Tim Mattson (Human Learning Group)

Room: 407 Jadwin Hall

Performance Tuning of Scientific Software

• Vassil Vasilev (Princeton University (US))

Room: 407 Jadwin Hall

Introduction to Automatic Differentiation and Differentiable Programming

• Vassil Vasilev (Princeton University (US))

Room: 407 Jadwin Hall

Columnar Data Analysis

• Tai Sakuma (Princeton University)
• Andres Rios-Tascon (Princeton University)

Room: 407 Jadwin Hall Data analysis languages, such as Numpy, MATLAB, R, IDL, and APL, are typically interactive with an array-at-a-time interface. Instead of performing an entire analysis in a single loop, each step in the calculation is a separate pass, letting the user inspect distributions each step of the way. Unfortunately, these languages are limited to primitive data types: mostly numbers and booleans. Variable-length and nested data structures, such as different numbers of particles per event, don't fit this model. Fortunately, the model can be extended. This tutorial will introduce awkward-array, the concepts of columnar data structures, and how to use them in data analysis, such as computing combinatorics (quantities depending on combinations of particles) without any for loops.

Columnar Data Analysis

• Andres Rios-Tascon (Princeton University)
• Tai Sakuma (Princeton University)

Room: 407 Jadwin Hall

Machine Learning - Advanced neural net models

• Liv Helen Vage (Princeton University (US))
• Michelle Kuchera (Davidson College)

Room: 407 Jadwin Hall There are lots of flavours of neural nets depending on the problem we are trying to solve. This lecture looks at convolutional neural nets, graph neural nets and transformers. We also have a brief glance at modern LLMs and other generative models.

Machine Learning

• Liv Helen Vage (Princeton University (US))
• Michelle Kuchera (Davidson College)

Room: 407 Jadwin Hall Time to work on the kaggle competition

GPU Programming

• Brij Kishor Jashal (Rutherford Appleton Laboratory)

Room: 407 Jadwin Hall

GPU Programming

• Brij Kishor Jashal (Rutherford Appleton Laboratory)

Room: 407 Jadwin Hall

GPU Programming

• Brij Kishor Jashal (Rutherford Appleton Laboratory)

Room: 407 Jadwin Hall

You are qualified to be teachers!

• Sudhir Malik (University of Puerto Rico (US))

Room: 407 Jadwin Hall Join IRIS-HEP/HSF Training!

Machine Learning Wrapup

• Liv Helen Vage (Princeton University (US))
• Michelle Kuchera (Davidson College)

Room: 407 Jadwin Hall There will be a brief presentation of some of the current and ongoing research on ML in HEP. After this, we look at the results of the Kaggle competition.

You're the Client Now: Rapid Unscheduled Prototyping with AI Coding Tools

• Mattie Niznik (Princeton University)

Room: 407 Jadwin Hall

Closing Session Room: 407 Jadwin Hall