Excluding national events, internal ATLAS collaboration events, \u00a0and presentations from co-authors. Most important ones are highlighted.<\/p>\n
\u00a0<\/span>“Recent progresses in using Artificial Intelligence for Particle Physics<\/strong><\/span>“, David Rousseau, Invited talk, American Physical Society April Meeting, Data Science and Machine Learning in Particle and Astrophysics session, virtual, April 2021<\/a><\/p>\n<\/li>\n \u00a0<\/span>“Using an Optical Processing Unit for tracking and calorimetry at the Large Hadron Collider” David Rousseau,\u00a0 <\/span>Fast Machine Learning for Science Workshop, SMU, Dallas (virtual), Dec 2020<\/a>\u00a0\u00a0<\/span><\/p>\n<\/li>\n “Using an Optical Processing Unit for tracking and calorimetry at the Large Hadron Collider<\/span>“, David Rousseau, \u00a0CERN Interexperiment Machine Learning Workshop, Oct 2020<\/a><\/p>\n<\/li>\n \u201cNovel data analysis technique<\/span>s\u201d,<\/strong> David Rousseau, plenary talk, European Physical Society Conference on High Energy Physics 2019<\/a>, Ghent, July 2019<\/p>\n<\/li>\n \u00a0<\/p>\n I’ve given many courses on the theme “Machine Learning and Particle Physics” in various “summer” \/ undergrad \/ grad schools, adapted to the audience, settings (on-site\/video\/hybrid) and duration (1 to 6 hours):<\/p>\n \u00a0<\/p>\n If possible given the format of the school, \u00a0I also run a tutorial on the basics of training and evaluating a Boosted Decision Tree or Neural Network classifier on a typical HEP dataset (4-vectors from Higgs to WW events from ATLAS Open Data). The tutorial is on github<\/a> (developed with Aishik Ghosh and J\u00e9r\u00e9my Couthures) and can readily be run on Google Colab, and also with minor mods run on one’s laptop with anaconda installation; I however do not recommend its use standalone as it requires background information (on ML, HEP and Higgs physics).<\/p>\n A bit off-topic : at the beginning of ATLAS, many students and experienced physicists as well need to be trained to use the ATLAS framework Athena, Event Data Model and algorithms. I’ve given many tutorials to that respect.<\/p>\n I also gave a course on “Physics at the LHC” at the VIII Mexican School for Particle Physics and Fields, Oaxaca, Mexico, Nov 1998 (AIP Conference Proceedings 490)<\/p>\n With Jean-Christophe Hamilton (CNRS APC) and \u00a0Guillaume Mention (CEA) I give a course since 2019 on “Statistics and Machine Learning for Cosmology and Particle Physics” to first year (pre-master)\u00a0CentraleSupelec<\/a> (Universit\u00e9 Paris-Saclay) students. About 120 students who have chosen the Physics and NanoTechnology (PNT) theme. I teach the Machine Learning part (16 hours). In addition to courses and tutorials, we have \u00a0one week full-time “Enseignement d’Int\u00e9gration” where students in “Collaboration” of 25 (5 teams of 5) have to build a complete ML analysis pipeline of an LHC dataset.<\/p>\n \u00a0 Conferences Excluding national events, internal ATLAS collaboration events, \u00a0and presentations from co-authors. Most important ones are highlighted. “AI for Particle Physics”, David Rousseau, AI4X Conference, Singapore, July 2025 “Fair Universe 2024: HiggsML Uncertainty Challenge“ David Rousseau et al. EUCaifCon, Amsterdam,\u00a0May 2024 “Fair Universe 2024: HiggsML Uncertainty Challenge” David Rousseau et al. Interexperiment Machine Learning … <\/p>\n\n
Seminars<\/h2>\n
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