mlsanityData to 14 Oct 2026

Data Science and Machine Learning in Education

arXiv 2207.09060 ↗First posted 19 Jul 2022physics.ed-ph cs.LG hep-excited by 3

Not on the map. Why?

Abstract (arXiv)

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 95 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

25 of 95 references.

References · 25

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13 more
  1. 2018Machine Learning in High Energy Physics Community White Papercited by 49
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  2. 2022FAIR AI Models in High Energy Physicscited by 4
  3. 2024Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practicecited by 0