{"id":271,"date":"2021-04-19T14:37:23","date_gmt":"2021-04-19T14:37:23","guid":{"rendered":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/?page_id=271"},"modified":"2026-04-21T12:21:08","modified_gmt":"2026-04-21T10:21:08","slug":"extension-hal-ccsd-2","status":"publish","type":"page","link":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/en\/extension-hal-ccsd-2\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<p>Selected publications. I highlighted the most important ones to my mind. See also some <a href=\"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/en\/outreach\/\">outreach publications in french<\/a>.<\/p>\n<h2>Articles<\/h2>\n<p>Most recent, full reference is below: the Fair Universe NeurIPS 2025 paper is publisheded! The two ATLAS papers using Neural Simulation Based Inference are published, the book &#8220;AI Competitions and Benchmarks: The Science Behind the Contests&#8221; is being finalised, check out the chapter &#8220;Towards impactful challenges : post-challenge paper, benchmarks and other dissemination actions&#8221;, \u00a0the book &#8220;Artificial Intelligence for High Energy Physics&#8221; <a href=\"https:\/\/doi.org\/10.1142\/12200\">doi:10.1142\/12200<\/a>\u00a0 is available, &#8220;<\/p>\n<h3>Reviews<\/h3>\n<ul>\n<li>David Rousseau, &#8220;AI for Particle Physics&#8221;, in S. Dale et al., &#8220;AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions&#8221;, <a href=\"https:\/\/arxiv.org\/abs\/2511.20976\">arXiv:2511.20976<\/a><\/li>\n<li>Antoine Marot, David Rousseau and Zhen Xu, &#8220;<span style=\"font-size: 14pt\"><strong>Towards impactful challenges : post-challenge paper, benchmarks and other dissemination actions<\/strong><\/span>&#8220;, \u00a0<a href=\"https:\/\/arxiv.org\/abs\/2312.06036\">arXiv:2312.06036<\/a>, to be published in book &#8220;AI Competitions and Benchmarks: the science behind the contests&#8221;, see <a href=\"https:\/\/ai-competitions-book.github.io\">this page<\/a><\/li>\n<li>David Rousseau, &#8220;<span style=\"font-size: 14pt\"><strong>Experimental Particle Physics and Artificial Intelligence&#8221;<\/strong><\/span>, \u00a0in book &#8220;Artificial Intelligence for Science&#8221;, <a href=\"https:\/\/www.worldscientific.com\/worldscibooks\/10.1142\/13123\" target=\"_blank\" rel=\"noopener\">doi:10.1142\/13123,\u00a0<\/a>World Scientific Publishing, 2023<\/li>\n<li>David Rousseau and \u00a0Andrey Ustyuzhanin,, &#8220;<span style=\"font-size: 14pt\"><strong>Machine Learning scientific competitions and datasets<\/strong><\/span>&#8221; \u00a0<a href=\"https:\/\/arxiv.org\/abs\/2012.08520\">arXiv:2012.08520<\/a>, in book &#8220;Artificial Intelligence for High Energy Physics&#8221;, <a href=\"https:\/\/doi.org\/10.1142\/12200\">doi:10.1142\/12200<\/a> World Scientific Publishing, 2023<\/li>\n<li>David Rousseau, &#8220;<span style=\"font-size: inherit\">Resource-efficient inference for particle physics&#8221;, <a href=\"https:\/\/www.nature.com\/articles\/s42256-021-00381-4\" target=\"_blank\" rel=\"noopener\"><i>Nat Mach Intell<\/i> <b>3, <\/b>656\u2013657 (2021)<\/a> (<a href=\"https:\/\/rdcu.be\/cuyuQ\" target=\"_blank\" rel=\"noopener\">author shareable link<\/a>), <em>which is a &#8220;News and Views&#8221; piece I was invited to write after reviewing the excellent<\/em>\u00a0&#8220;Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors&#8221; Coelho et al <a href=\"https:\/\/www.nature.com\/articles\/s42256-021-00356-5\" target=\"_blank\" rel=\"noopener\"><i>Nat Mach Intell<\/i> <b>3, <\/b>675\u2013686 (2021)<\/a>\u00a0<em>(a cooperation between CERN and Google), which turned out to make it to the cover of <a href=\"https:\/\/www.nature.com\/natmachintell\/volumes\/3\/issues\/8\" target=\"_blank\" rel=\"noopener\">this issue<\/a>.<\/em><\/span><\/li>\n<li>David Rousseau, &#8220;Connecting the dots to track particles in high energy physics\u201d Nature Machine Intelligence, <a href=\"https:\/\/www.nature.com\/articles\/s42256-019-0061-0\" target=\"_blank\" rel=\"noopener\">Nature Machine Intelligence 1, 288 (2019)<\/a>, <em>which is a &#8220;Challenge accepted&#8221; piece about the TrackML challenge.<\/em><\/li>\n<li>Alexander Radovic, Michael Williams, David Rousseau, Michael Kagan, \u00a0et al.<span class=\"Apple-converted-space\">\u00a0 <\/span>(2018). <strong><span style=\"font-size: 14pt\">&#8220;Machine learning at the energy and intensity frontiers of particle physics&#8221;.<\/span><\/strong> <a href=\"https:\/\/www.nature.com\/articles\/s41586-018-0361-2\">Nature, 560(7716), 41<\/a>, (<a href=\"https:\/\/www.nature.com\/articles\/s41586-018-0361-2.epdf?author_access_token=VEOIa5y8zWfWWJp4D_BjONRgN0jAjWel9jnR3ZoTv0Og5Mk-g4lLvg4gLtNz3x_LFHh_fP7KAoOOXqUsKjsx57yhoo54qQ1hyCajOWEw794WUGLt_LeYc6ZHhxpLySNS2efgsRU8WQBJGuRh7ZLQ4g==\">author shareable link<\/a>),\u00a0<em>The field of ML for HEP was just emerging.<\/em><\/li>\n<\/ul>\n<h3>Machine Learning<\/h3>\n<div class=\"page\" title=\"Page 15\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<ul>\n<li>Wahid Bhimji et al., <strong>&#8220;<span style=\"font-size: 14pt\">FAIR Universe HiggsML Uncertainty Challenge Competition<\/span>&#8221; , <\/strong>NeurIPS 2025 Datasets and Benchmarks Track, <a href=\"http:\/\/NeurIPS 2025 Datasets and Benchmarks Track\" data-wplink-url-error=\"true\">OpenReview<\/a><strong> \u00a0,<\/strong><a href=\"https:\/\/arxiv.org\/abs\/2410.02867\">arXiv:2410.02867<\/a>. \u00a0 <em>description of our NeurIPS 2024 competition and dataset<\/em>\u00a0(Ragansu Chakkappai&#8217;s PhD thesis).<\/li>\n<li>The ATLAS collaboration, &#8220;<span style=\"font-size: 14pt\"><strong>An implementation of neural simulation-based inference for parameter estimation in ATLAS<\/strong><\/span>&#8220;, Rep. Prog. Phys. 88 (2025) 067801, <a href=\"https:\/\/arxiv.org\/abs\/2412.01600\">arXiv:2412.01600<\/a> . <em>Many details on the technique used in the corresponding physics paper<\/em><\/li>\n<li>The ATLAS collaboration, &#8220;<span style=\"font-size: 14pt\"><strong>Deep generative models for fast photon shower simulation in ATLAS<\/strong><\/span>&#8220;, <a href=\"https:\/\/arxiv.org\/abs\/2210.06204\" target=\"_blank\" rel=\"noopener\">arXiv:2210.06204<\/a>, Comput. Softw. Big Sci. 8 (2024) 1, 7. <em>This is the first paper on simulating a real calorimeter with deep generative models, GAN (Aishik Ghosh&#8217;s PhD) or VAE.<\/em><\/li>\n<li>Sabrina Amrouche et al., <strong><span style=\"font-size: 14pt\">&#8220;The Tracking Machine Learning challenge : Throughput phase&#8221;<\/span><\/strong>,Comput Softw Big Sci 7, <strong>1<\/strong> (2023), <a href=\"https:\/\/arxiv.org\/abs\/2105.01160\">arXiv:2105.01160\u00a0<\/a><em>The final paper for the TrackML challenge with lessons both in terms of algorithm and challenge organisation.<\/em><\/li>\n<\/ul>\n<div class=\"page\" title=\"Page 15\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<ul>\n<li>Sabrina Amrouche et al. \u201dThe Tracking Machine Learning Challenge : Accuracy phase\u201d NeurIPS 2018 Competition Book, within Springer Series on Challenges in Machine Learning <a href=\"https:\/\/arxiv.org\/abs\/1904.06778\">arXiv:1904.06778.<\/a> <em>The paper on the first phase TrackML challenge, about accuracy without any throughput constraints.<\/em><\/li>\n<li>\n<p><span style=\"font-size: inherit\">Victor Estrade, C\u00e9cile Germain, Isabelle Guyon, David Rousseau. &#8220;<span style=\"font-size: 14pt\">Systematics aware learning: a case study in High Energy Physics<\/span>&#8220;. Proceedings, 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) : Sofia, Bulgaria, July 9-13, 2018,\u00a0<a href=\"https:\/\/doi.org\/10.1051\/epjconf\/201921406024\">EPJ Web Conf. 214 (2019) 06024<\/a>\u00a0<em>This paper has set up a framework for the study of the best strategies to take into account systematics uncertainties at training time instead of post-hoc. A very active field.<\/em><\/span><\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<ul>\n<li>\u201cTrack reconstruction at LHC as a collaborative data challenge use case with RAMP\u201d Sabrina Amrouche et al., in proceedings \u201dConnecting The Dots\/Trackers\u201d 2017 (CTD\/WIT 2017) at Orsay, 6-9th March 2017, <a href=\"https:\/\/www.epj-conferences.org\/articles\/epjconf\/abs\/ 2017\/19\/epjconf_ctdw2017_00015\/epjconf_ctdw2017_00015.html\" data-wplink-url-error=\"true\">EPJ Web Conf., 150 (2017) 00015<\/a>, AIDA-2020-CONF-2017-<br \/>002,\u00a0<\/li>\n<li>&#8220;<strong><span style=\"font-size: 14pt\">The Higgs boson machine learning challenge<\/span><\/strong>\u201d, Claire Adam-Bourdarios, Glen Cowan, Balazs K\u00e9gl, C\u00e9cile Germain, Isabelle Guyon et David Rousseau in \u201cProceedings, 28th Annual Conference on Neural Information Processing Systems (NIPS 2014) : Montreal, Quebec, Canada, December 8-13, 2014\u201d, \u0301edited Glenn Cowan, Balazs K\u00e9gl, C\u00e9cile Germain, Isabelle Guyon et David Rousseau <a href=\"http:\/\/proceedings.mlr.press\/v42\/cowa14.html\">PMLR 42<\/a>\u00a0<em>The main paper summarising the lessons from the HiggsML challenge.<\/em><\/li>\n<\/ul>\n<h3>Visualisation<\/h3>\n<div class=\"page\" title=\"Page 15\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<ul>\n<li>Xiyao Wang, Lonni Besan\u00e7on, David Rousseau, Mickael Sereno, Mehdi Ammi, Tobias Isenberg \u201dTowards an Understanding of Augmented Reality Extensions for Existing 3D Data Analysis Tools\u201d, CHI \u201920: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems April 2020 <a href=\"http:\/\/doi.org\/10.1145\/3313831.3376657\">Pages 1\u201313 <\/a><\/li>\n<\/ul>\n<p><em>This was an unexpected spin-off from the TrackML challenge. Part of X. Wang&#8217;s PhD (supervised by T. Isenberg) was to develop a visualisation of the TrackML dataset using Augmented Reality on MS Hololens.<\/em><\/p>\n<p><span style=\"color: inherit;font-family: inherit;font-size: 24px;font-weight: bold\">Software<\/span><\/p>\n<div class=\"page\" title=\"Page 15\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<ul>\n<li>\n<p>&#8220;A Common Tracking Software Project&#8221; Xiaocong Ai et al., Comput Softw Big Sci 6, <strong>8<\/strong> (2022), <a href=\"https:\/\/arxiv.org\/abs\/2106.13593\">arXiv:2106.13593<\/a><\/p>\n<\/li>\n<li>\n<p>David Rousseau, \u201c<strong><span style=\"font-size: 14pt\">The Software behind the Higgs Boson Discovery<\/span><\/strong>,\u201d <a href=\"http:\/\/hal. in2p3.fr\/in2p3-00735717\" data-wplink-url-error=\"true\">IEEE Software, vol. 29, no. 5, pp. 11-15, Sept.-Oct. 2012<\/a>. <em>This gave me the opportunity to describe briefly both in terms of algorithms and organisation how we (circa 1000 ATLAS physicists, software engineers and students over 10 years) manage to develop and deploy ATLAS software.\u00a0<\/em><\/p>\n<\/li>\n<li><span style=\"font-size: inherit\">Michael Hodgkinson et al. [ATLAS Collaboration], \u201cSoftware validation in ATLAS,\u201d <a href=\"https:\/\/doi.org\/10.1088\/1742-6596\/396\/5\/052040\">J. Phys. Conf. Ser. 396 (2012) 052040,<\/a> ATL-SOFT-PROC- 2012-050.<\/span><\/li>\n<li><span style=\"font-size: inherit\">O. Aidel,\u00a0S. Albrand, \u00a0G. Dimitrov, \u00a0D. Rousseau, R. D. Schaffer and\u00a0I. Vukotic \u201cMonitoring of computing resource utilization of the ATLAS ex<\/span><span style=\"font-size: inherit\">periment,\u201d <a href=\"https:\/\/doi.org\/10.1088\/1742-6596\/396\/3\/032112\">J. Phys. Conf. Ser. 396 (2012) 032112<\/a>, ATL-SOFT-PROC- 2012-034.<\/span><\/li>\n<li><span style=\"font-size: inherit\">G. Aad et al. [ATLAS Collaboration], \u201cThe ATLAS Simulation Infrastructure,\u201d Eur. Phys. J. C 70 (2010) 823 <a href=\"https:\/\/arxiv.org\/abs\/1005.4568\">arXiv:1005.4568<\/a><\/span><\/li>\n<li><span style=\"font-size: inherit\">E. Obreshkov et al., \u201cOrganization and management of ATLAS software releases,\u201d <a href=\"http:\/\/weblib.cern.ch\/abstract?ATL-SOFT-PUB-2006-008\">Nucl. Instrum. Meth. A 584 (2008) 244.<\/a><\/span><\/li>\n<\/ul>\n<\/div>\n<h3>ATLAS Physics<\/h3>\n<div class=\"page\" title=\"Page 8\">\n<div class=\"layoutArea\">\n<div class=\"column\">\n<ul>\n<li>The ATLAS collaboration, &#8220;<span style=\"font-size: 14pt\"><strong>Measurement of off-shell Higgs boson production in the H*\u2192ZZ\u21924\u2113 \u00a0decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector<\/strong><\/span>&#8220;, \u00a0Rep. Prog. Phys. 88 (2025) 057803, <a href=\"https:\/\/arxiv.org\/abs\/2412.01548\">arXiv:2412.01548<\/a>. <em>First use of NSBI at the LHC, with a clear sensitivity improvement (with contribution from Arnaud Maury&#8217;s PhD).<\/em><\/li>\n<li>M. Aaboud et al. [ATLAS Collaboration] &#8220;Constraints on off-shell Higgs boson production and the Higgs boson total width in ZZ\u21924\u2113\u00a0and ZZ\u21922\u21132\u03bd\u00a0final states with the ATLAS detector&#8221;,\u00a0Phys. Lett. B 786 (2018) 223-244, <a href=\"https:\/\/arxiv.org\/abs\/1808.01191\">arXiv:1808.01191<\/a><\/li>\n<li>Georges Aad et al. [ATLAS Collaboration] &#8220;<strong><span style=\"font-size: 14pt\">Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector<\/span><\/strong>&#8220;, JHEP 04 (2015) 117, <a href=\"https:\/\/arxiv.org\/abs\/1501.04943\">arXiv:1501.04943\u00a0<\/a><em>The channel is a very difficult one, I contributed with a Markov Chain Monte-Carlo which was instrumental in the estimation of the event by event Higgs boson mass.<\/em><\/li>\n<li>\u201cLittle Higgs studies with ATLAS\u201d, E. Ros and D.Rousseau ATL- PHYS-CONF-2006-007; ATL-COM-PHYS-2006-031 in \u201cWorkshop on CP Studies and Non-Standard Higgs Physics\u201d E. Accomando et al. CERN-2006-009, <a href=\"https:\/\/arxiv.org\/abs\/0608079\">arXiv:hep-ph\/0608079<\/a>, p326-336<\/li>\n<\/ul>\n<\/div>\n<h3>ALEPH Physics<\/h3>\n<ul>\n<li><span style=\"font-size: inherit\">R. Barate et al. [ALEPH Collaboration], &#8220;Search for the B<\/span><sub>c<\/sub><span style=\"font-size: inherit\"> meson in hadronic Z decays&#8221; <a href=\"https:\/\/doi.org\/10.1016\/S0370-2693(97)00461-9\">Physics Letters B402, 213 (1997)<\/a> \u00a0 B<em>est limit before the discovery of this particle at CDF. The <\/em><\/span><span style=\"font-size: inherit\">B<\/span><sub>c\u00a0<\/sub><span style=\"font-size: inherit\"><em>is now routinely studied the LHC.<\/em><\/span><\/li>\n<li>D. Buskulic et al. [ALEPH Collaboration],&#8221;<span style=\"font-size: 14pt\"><strong>Measurements of |V(cb)|, form-factors and branching fractions in the decays anti-B0 -&gt; D*+ lepton- anti-lepton-neutrino and anti-B0 -&gt; D+ lepton- anti-lepton-neutrino<\/strong><\/span>&#8220;,\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S0370-2693(97)00071-3\">Physics Letters B395, 373 (1997)<\/a>\u00a0O<em>nly such measurement at LEP, before it was done much more precisely at the B factories, which have shown much later that the theoretical basis was flawed&#8230;\u00a0<\/em><\/li>\n<li>D. Buskulic et al. [ALEPH Collaboration], &#8220;A measurement of form factors and |V<sub>cb<\/sub>| from in B<sup>0\u00a0<\/sup>D<sup>*+<\/sup>lnu&#8221; \u00a0<a href=\"https:\/\/doi.org\/10.1016\/0370-2693(95)01021-H\">Physics Letters B 359, 236 (1995)<\/a>\u00a0 <em>First such measurement at LEP.\u00a0<\/em><\/li>\n<\/ul>\n<ul>\n<li>D. Buskulic et al. [ALEPH Collaboration], &#8220;<strong><span style=\"font-size: 14pt\">Measurement of the b hadron lifetime in the J\/\u03c8 channel at ALEPH<\/span><\/strong>&#8220;,\u00a0<a href=\"https:\/\/doi.org\/10.1016\/0370-2693(92)91581-S\">Physics Letters B295, 396 (1992)<\/a>. <em>First measurement at LEP using full 3-D vertexing. This was the beginning of Silicon detectors, now the basis of tracking at the LHC. The main paper from my PhD.<\/em><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2>Book edition<\/h2>\n<ul>\n<li>Proceedings, EUCaifCon 2025, Cagliari : in production<\/li>\n<li>&#8220;<strong><span style=\"font-size: 14pt\">Artificial Intelligence for High Energy Physics<\/span><\/strong>&#8220;, World Scientific Publishing, edited by Paolo Calafiura, David Rousseau and Kazuhiro Terao, <a href=\"https:\/\/doi.org\/10.1142\/12200\">doi:10.1142\/12200<\/a><span style=\"font-size: inherit\">\u00a0March 2022,\u00a0<em>We&#8217;ve collected 20 chapters from the best experts of this now established field.<\/em><\/span><\/li>\n<li><span style=\"font-size: inherit\">\u201cProceedings, Connecting The Dots \/ Intelligent\u00a0<\/span><span style=\"font-size: inherit\">Tracker (CTD\/WIT 2017) : Orsay, France, March 6-9, 2017,\u201d edited by C. Germain, H. Grasland, A. Lounis, D. Rousseau and D. Varouchas, \u00a0<a href=\"https:\/\/www.epj-conferences. org\/articles\/epjconf\/abs\/2017\/19\/contents\/contents.html\" data-wplink-url-error=\"true\">EPJ Web Conf. 150 (2017)<\/a><\/span><\/li>\n<li>\n<p>\u201cProceedings, NIPS 2014 Workshop on High-energy Physics and Machine Learning, 13 December 2014, Montreal, Canada\u201d, edited par G. Cowan, B. K\u00e9gl, C. Germain, I. Guyon et D. Rousseau, <a href=\"http:\/\/proceedings.mlr.press\/v42\">Proceedings of Machine Learning Research 42<\/a><\/p>\n<div class=\"page\" title=\"Page 5\">\u00a0<\/div>\n<\/li>\n<\/ul>\n<h2>Datasets<\/h2>\n<ul>\n<li>Bhimji, W., Calafiura, P., Chakkappai, R., Chang, P.-W., Chou, Y.-T., Diefenbacher, S., Dudley, J., Farrell, S., Ghosh, A., Guyon, I., Harris, C., Hsu, S.-C., Elham E Khoda, Nachman, B., Nugent, P., Rousseau, D., Thorne, B., Ullah, I., &amp; Zhang, Y. (2025). <strong>FAIR Universe &#8211; HiggsML Uncertainty Challenge Public Dataset<\/strong> [Data set]. Neural Information Processing Systems (NeurIPS) 2025, Vancouver, Canada. Zenodo. <a href=\"https:\/\/doi.org\/10.5281\/zenodo.15131565\">doi:10.5281\/zenodo.15131565<\/a>\u00a0<\/li>\n<li>Sabrina Amrouche et al,\u00a0 &#8220;<strong><span style=\"font-size: 14pt\">TrackML throughput phase dataset<\/span><\/strong>&#8220;, Sept 2018,\u00a0<a href=\"https:\/\/doi.org\/10.5281\/zenodo.4730157\">doi:10.5281\/zenodo.4730157<\/a>.<\/li>\n<li>Sabrina Amrouche et al,\u00a0&#8220;TrackML particle tracking challenge Accuracy phase dataset&#8221;, Aug 2018,<a href=\"https:\/\/doi.org\/10.5281\/zenodo.4730167\">doi:10.5281\/zenodo.4730167<\/a><\/li>\n<\/ul>\n<h2>Thesis<\/h2>\n<p>David Rousseau. &#8220;Mesure de la duree de vie des hadrons B dans le canal de desintegration en J\/\u03a8 \u00e0\u00a0l&#8217;experience ALEPH au LEP&#8221; Universit\u00e9 de la M\u00e9diterran\u00e9e &#8211; Aix-Marseille II, 1992. in french. \u27e8<a href=\"http:\/\/hal.in2p3.fr\/in2p3-00010922\">in2p3-00010922<\/a>\u27e9 (document unavailable in electronic form)<\/p>\n<h2>Habilitation \u00e0 Diriger les Recherches<\/h2>\n<p>David Rousseau &#8220;Recherche du \u226aPetit Higgs\u226b et d\u00e9veloppement des algorithmes de reconstruction dans ATLAS.&#8221; Universit\u00e9 Paris Sud &#8211; Paris XI, 2007. <a href=\"https:\/\/tel.archives-ouvertes.fr\/tel-00529499\" target=\"_blank\" rel=\"noopener\">\u27e8tel-00529499\u27e9<\/a><\/p>\n<h2>Editorial activity<\/h2>\n<p>To paraphrase Churchill on democracy, I believe the current peer-review system is the worst system, except for all the others. Many times I&#8217;ve downloaded a vaguely interesting arXiv paper only to realise it was a waste of time. But of course, there are also unpublished landmark papers.<\/p>\n<p>I&#8217;ve been associate editor for \u00a0<a href=\"https:\/\/www.springer.com\/journal\/41781\">Computing and Software for the Big Science<\/a> 2019-2024 and now 2025- for <a href=\"https:\/\/epjc.epj.org\/epjc-editorial-board\">European Physical Journal C<\/a><\/p>\n<p>I have served as a reviewer for: Baylearn, Computing for High Energy Physics, Computer Physics Communication, Communication Physics, Computing and Software for the Big Science, Connecting The Dots, \u00a0European Physics Journal C, IEEE Transaction in Nuclear Science, Journal of High Energy Physics, Journal of Instrumentation in Nuclear Science, Machine Learning 4 the Physical Science (NeurIPS), Nature Communications, Nature Machine Intelligence, Nature Review Physics , NeurIPS Competition, Nuclear Instruments and Methods A, Physics Review D, Physics Review Letters, Reviews in Physics, Teaching Machine Learning ( ECML ).<\/p>\n<p>I have served as an expert for grant reviews in Belgium, ERC, France, Germany, Portugal, Switzerland and UK.<\/p>\n<h2>All articles<\/h2>\n<p>As a particle physicist, I have about 1300 \u00a0publications through my experiments ALEPH (1991-2000) and ATLAS (1998-). \u00a0This may be surprising for people outside the field; the logic behind is that big experiments like these require many people to spend time designing, commissioning and running the detector, including all hardware and software components which is rarely suitable for publications; this was in fact my case for the 12 years I spent developing ATLAS offline software.<\/p>\n<p><a href=\"https:\/\/inspirehep.net\/literature?sort=mostrecent&amp;size=25&amp;page=1&amp;q=a%20D.Rousseau.1\">All my publications in inspireHep<\/a> (no false positive I&#8217;m aware of, a few non-HEP publications missing). <a href=\"https:\/\/inspirehep.net\/literature?sort=mostrecent&amp;size=25&amp;page=1&amp;q=find%20author%20D.Rousseau.1%20and%20not%20cn%20atlas%20and%20not%20cn%20aleph\" target=\"_blank\" rel=\"noopener\">All my publications in inspireHep excluding the ones from ALEPH and ATLAS<\/a>.<\/p>\n<p><a href=\"https:\/\/cv.archives-ouvertes.fr\/david-rousseau\" target=\"_blank\" rel=\"noopener\">All my publications in HAL <\/a>(completeness not guaranteed and a number of false hits due to homonyms, there are (at least) two other publishing David Rousseau, one at Universit\u00e9 Angers (also doing ML!), and one at IFPEN)<\/p>\n<p><a href=\"https:\/\/www.researchgate.net\/profile\/David-Rousseau\">All my publications on Researchgate\u00a0<\/a>and\u00a0<a href=\"https:\/\/scholar.google.com\/citations?user=Xzdm_9IAAAAJ&amp;hl=fr\">All my publications on Google Scholar\u00a0<\/a>are not curated.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Selected publications. I highlighted the most important ones to my mind. See also some outreach publications in french. Articles Most recent, full reference is below: the Fair Universe NeurIPS 2025 paper is publisheded! The two ATLAS papers using Neural Simulation Based Inference are published, the book &#8220;AI Competitions and Benchmarks: The Science Behind the Contests&#8221; &hellip; <\/p>\n<p><a class=\"more-link btn\" href=\"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/en\/extension-hal-ccsd-2\/\">Continue reading<\/a><\/p>\n","protected":false},"author":18,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-onecolumn.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-271","page","type-page","status-publish","hentry","nodate","item-wrap"],"acf":[],"_links":{"self":[{"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/pages\/271","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/comments?post=271"}],"version-history":[{"count":52,"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/pages\/271\/revisions"}],"predecessor-version":[{"id":708,"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/pages\/271\/revisions\/708"}],"wp:attachment":[{"href":"https:\/\/users.ijclab.in2p3.fr\/david-rousseau\/wp-json\/wp\/v2\/media?parent=271"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}