Eighth Computational and Data Science school for HEP (CoDaS-HEP 2026)

13 Jul 2026, 08:30 → 17 Jul 2026, 14:00 US/Eastern

407 Jadwin Hall (Princeton University)

The eighth school on tools, techniques and methods for Computational and Data Science for High Energy Physics (CoDaS-HEP 2025) will take place on 13-17 July, 2026, at Princeton University.

Advanced software is a critical ingredient to scientific research. Training young researchers in the latest tools and techniques is an essential part of developing the skills required for a successful career both in research and in industry.

The CoDaS-HEP school aims to provide a broad introduction to these critical skills as well as an overview of applications High Energy Physics. Specific topics to be covered at the school include:

• Parallel Programming 
• Big Data Tools and Techniques
• Machine Learning 
• Practical skills like performance evaluation, collaborative use of git/github, etc.

The school offers a limited number of young researchers an opportunity to learn these skills from experienced scientists and instructors. Successful applicants will receive travel and lodging support to attend the school.

School website: http://codas-hep.org   

The school lectures will take place in 407 Jadwin Hall, in the main lecture hall of the Princeton Center for Theoretical Science (PCTS).

This event is supported by National Science Foundation grants PHY-2323298, OAC-2103945, the Princeton Institute for Computational Science and Engineering (PICSciE), the Princeton Physics Department, the Office of the Dean for Research of Princeton University and the Enrico Fermi Institute at the University of Chicago. Any opinions, findings, conclusions or recommendations expressed in this material are those of the developers and do not necessarily reflect the views of the National Science Foundation.

 

Monday 13 July

08:30 → 09:00 Breakfast

09:00 → 09:10 Welcome and Overview

Speaker: Peter Elmer (Princeton University (US))

09:10 → 10:30 Collaborative Software Development with Git(Hub)

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.

Speaker: David Lange (Princeton University (US))

10:30 → 11:00 Coffee Break

11:00 → 11:30 Getting connected to our compute platform

Speaker: David Lange (Princeton University (US))

11:30 → 12:30 What Every Computational Physicist Should Know About Computer Architecture

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.

Speaker: David Lange (Princeton University (US))

12:30 → 13:30 Lunch

13:30 → 15:30 The Scientific Python Ecosystem

In recent years, Python has become a glue language for scientific computing. Although code written in Python is generally slow, it has a good connection with compiled C code and a common data abstraction through Numpy. Many data processing, statistical, and most machine learning software has a Python interface as a matter of course.

This tutorial will introduce you to core Python packages for science, such as NumPy, SciPy, Matplotlib, Pandas, and Numba, (part 1) as well as HEP-specific tools like iminuit, particle, pyjet, and pyhf (part 2). We'll especially focus on accessing ROOT data in uproot and awkward. Part 1 will also cover the Scientific Python Development Guide and a short discussion on packaging.

Speakers: Ianna Osborne (Princeton University), Andres Rios-Tascon (Princeton University)

15:30 → 16:00 Coffee Break

16:00 → 17:30 The Scientific Python Ecosystem

Continued from last time. Part 2 focuses on the HEP portion of the ecosystem.

Speakers: Ianna Osborne (Princeton University), Andres Rios-Tascon (Princeton University)

18:00 → 20:30 Welcome Reception - D301, Commons (near Briger Hall), Ivy Lane

Tuesday 14 July

08:30 → 09:00 Breakfast

09:00 → 11:00 An Introduction to Parallel Programming with OpenMP

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).

Speaker: Tim Mattson (Intel-Retired)

11:00 → 11:10 Group photo: Jadwin hall plaza

11:10 → 11:30 Coffee Break

11:30 → 13:00 OpenMP and parallel programming beyond shared memory CPUs

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.

Speaker: Tim Mattson (Intel-Retired)

13:00 → 14:00 Lunch

14:00 → 15:30 Introduction to Machine Learning

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.

Speakers: Liv Helen Vage (Princeton University (US)), Michelle Kuchera (Davidson College)

15:30 → 16:00 Coffee Break

16:00 → 18:00 Machine learning - Neural networks & Kaggle

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.

Speakers: Liv Helen Vage (Princeton University (US)), Michelle Kuchera (Davidson College)

18:30 → 21:00 Reception - Prospect House

Wednesday 15 July

08:30 → 09:00 Breakfast

09:00 → 10:00 Floating Point Arithmetic Is Not Real

Speaker: Tim Mattson (Intel)

10:00 → 11:00 The Use and Abuse of Random Numbers

Speaker: Tim Mattson (Human Learning Group)

11:00 → 11:30 Coffee Break

11:30 → 12:15 TBD

12:15 → 13:00 TBD

13:00 → 14:00 Lunch

14:00 → 15:30 Columnar Data Analysis

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.

Speaker: Andres Rios-Tascon (Princeton University)

15:30 → 16:00 Coffee Break

16:00 → 18:00 Columnar Data Analysis

Speaker: Andres Rios-Tascon (Princeton University)

19:00 → 21:00 Dinner on your own

Thursday 16 July

08:30 → 09:00 Breakfast

09:00 → 10:30 Machine Learning - Advanced neural net models

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.

Speakers: Liv Helen Vage (Princeton University (US)), Michelle Kuchera (Davidson College)

10:30 → 11:00 Coffee Break

11:00 → 12:00 Machine Learning

Time to work on the kaggle competition

Speakers: Liv Helen Vage (Princeton University (US)), Michelle Kuchera (Davidson College)

12:00 → 13:00 GPU Programming

Speaker: Brij Kishor Jashal (Rutherford Appleton Laboratory)

13:00 → 14:00 Lunch

14:00 → 16:00 GPU Programming

Speaker: Brij Kishor Jashal (Rutherford Appleton Laboratory)

16:00 → 16:30 Coffee Break

16:30 → 18:00 GPU Programming

Speaker: Brij Kishor Jashal (Rutherford Appleton Laboratory)

18:30 → 21:30 BBQ and Drinks - Palmer House

Friday 17 July

08:30 → 09:00 Breakfast

09:00 → 09:10 You are qualified to be teachers!

Join IRIS-HEP/HSF Training!

Speaker: Sudhir Malik (University of Puerto Rico (US))

09:10 → 10:10 Machine Learning Wrapup

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.

Speakers: Liv Helen Vage (Princeton University (US)), Michelle Kuchera (Davidson College)

10:10 → 11:00 TBD

11:00 → 11:30 Coffee Break

11:30 → 12:30 Closing Session