Publications

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 book “AI Competitions and Benchmarks: The Science Behind the Contests” is being finalised, check out the chapter “Towards impactful challenges : post-challenge paper, benchmarks and other dissemination actions”,  the book “Artificial Intelligence for High Energy Physics” doi:10.1142/12200  is available, ” Deep generative models for fast shower simulation in ATLAS” is finally accepted for publication by CSBS (previously released as an ATLAS paper) arXiv:2210.06204 CSBS reference to be updated

Reviews

  • Antoine Marot, David Rousseau and Zhen Xu, “Towards impactful challenges : post-challenge paper, benchmarks and other dissemination actions“,  arXiv:2312.06036, to be published in book “AI Competitions and Benchmarks: the science behind the contests”, see this page
  • David Rousseau,, “Experimental Particle Physics and Artificial Intelligence”,  in book “Artificial Intelligence for Science”, doi:10.1142/13123, World Scientific Publishing, 2023
  • David Rousseau and  Andrey Ustyuzhanin,, “Machine Learning scientific competitions and datasets”  arXiv:2012.08520, in book “Artificial Intelligence for High Energy Physics”, doi:10.1142/12200 World Scientific Publishing, 2023
  • David Rousseau, “Resource-efficient inference for particle physics”, Nat Mach Intell 3, 656–657 (2021) (author shareable link), which is a “News and Views” piece I was invited to write after reviewing the excellent “Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors” Coelho et al Nat Mach Intell 3, 675–686 (2021) (a cooperation between CERN and Google), which turned out to make it to the cover of this issue.
  • Alexander Radovic, Michael Williams, David Rousseau, Michael Kagan,  et al.  (2018). “Machine learning at the energy and intensity frontiers of particle physics”. Nature, 560(7716), 41, (author shareable link), The field of ML for HEP was just emerging.
  • David Rousseau, “Connecting the dots to track particles in high energy physics” Nature Machine Intelligence, Nature Machine Intelligence 1, 288 (2019), which is a “Challenge accepted” piece about the TrackML challenge.

Machine Learning

  • The ATLAS collaboration, “Deep generative models for fast photon shower simulation in ATLAS“, arXiv:2210.06204, to be published in Computing and Software for the Big Science. This is the first paper on simulating a real calorimeter with deep generative models, GAN (Aishik Ghosh’s PhD) or VAE.
  • Sabrina Amrouche et al., “The Tracking Machine Learning challenge : Throughput phase”,Comput Softw Big Sci 7, 1 (2023), arXiv:2105.01160 The final paper for the TrackML challenge with lessons both in terms of algorithm and challenge organisation.
  • Sabrina Amrouche et al. ”The Tracking Machine Learning Challenge : Accuracy phase” NeurIPS 2018 Competition Book, within Springer Series on Challenges in Machine Learning arXiv:1904.06778. The paper on the first phase TrackML challenge, about accuracy without any throughput constraints.

  • The ATLAS Collaboration “Deep generative models for fast shower simulation in ATLAS“ arXiv:2210.06204, submitted to Computing and Software for Big Science Proof that the complex ATLAS calorimeter can be simulated with a GAN (our work) or a VAE (U Geneva work).

  • Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau. “Systematics aware learning: a case study in High Energy Physics“. Proceedings, 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) : Sofia, Bulgaria, July 9-13, 2018, EPJ Web Conf. 214 (2019) 06024 This paper has set up a framework for the study of the best strateges to take into account systematics uncertainties at training time instead of post-hoc. A very active field.

  • “Track reconstruction at LHC as a collaborative data challenge use case with RAMP” Sabrina Amrouche et al., in proceedings ”Connecting The Dots/Trackers” 2017 (CTD/WIT 2017) at Orsay, 6-9th March 2017, EPJ Web Conf., 150 (2017) 00015, AIDA-2020-CONF-2017-
    002, 
  • “The Higgs boson machine learning challenge”, Claire Adam-Bourdarios, Glen Cowan, Balazs Kégl, Cécile Germain, Isabelle Guyon et David Rousseau in “Proceedings, 28th Annual Conference on Neural Information Processing Systems (NIPS 2014) : Montreal, Quebec, Canada, December 8-13, 2014”, ́edited Glenn Cowan, Balazs Kégl, Cécile Germain, Isabelle Guyon et David Rousseau PMLR 42 The main paper summarising the lessons from the HiggsML challenge.

Visualisation

  • Xiyao Wang, Lonni Besançon, David Rousseau, Mickael Sereno, Mehdi Ammi, Tobias Isenberg ”Towards an Understanding of Augmented Reality Extensions for Existing 3D Data Analysis Tools”, CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems April 2020 Pages 1–13

This was an unexpected spin-off from the TrackML challenge. Part of X. Wang’s PhD (supervised by T. Isenberg) was to develop a visualisation of the TrackML dataset using Augmented Reality on MS Hololens.

Software

  • “A Common Tracking Software Project” Xiaocong Ai et al., Comput Softw Big Sci 6, 8 (2022), arXiv:2106.13593

  • David Rousseau, “The Software behind the Higgs Boson Discovery,” IEEE Software, vol. 29, no. 5, pp. 11-15, Sept.-Oct. 2012. 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. 

  • Michael Hodgkinson et al. [ATLAS Collaboration], “Software validation in ATLAS,” J. Phys. Conf. Ser. 396 (2012) 052040, ATL-SOFT-PROC- 2012-050.
  • O. Aidel, S. Albrand,  G. Dimitrov,  D. Rousseau, R. D. Schaffer and I. Vukotic “Monitoring of computing resource utilization of the ATLAS experiment,” J. Phys. Conf. Ser. 396 (2012) 032112, ATL-SOFT-PROC- 2012-034.
  • G. Aad et al. [ATLAS Collaboration], “The ATLAS Simulation Infrastructure,” Eur. Phys. J. C 70 (2010) 823 arXiv:1005.4568
  • E. Obreshkov et al., “Organization and management of ATLAS software releases,” Nucl. Instrum. Meth. A 584 (2008) 244.

ATLAS Physics

  • M. Aaboud et al. [ATLAS Collaboration] “Constraints on off-shell Higgs boson production and the Higgs boson total width in ZZ→4ℓ and ZZ→2ℓ2ν final states with the ATLAS detector”, Phys. Lett. B 786 (2018) 223-244, arXiv:1808.01191
  • Georges Aad et al. [ATLAS Collaboration] “Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector“, JHEP 04 (2015) 117, arXiv:1501.04943 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.
  • “Little Higgs studies with ATLAS”, E. Ros and D.Rousseau ATL- PHYS-CONF-2006-007; ATL-COM-PHYS-2006-031 in “Workshop on CP Studies and Non-Standard Higgs Physics” E. Accomando et al. CERN-2006-009, arXiv:hep-ph/0608079, p326-336

ALEPH Physics

  • R. Barate et al. [ALEPH Collaboration], “Search for the Bc meson in hadronic Z decays” Physics Letters B402, 213 (1997)   Best limit before the discovery of this particle at CDF. The Bc is now routinely studied the LHC.
  • D. Buskulic et al. [ALEPH Collaboration],”Measurements of |V(cb)|, form-factors and branching fractions in the decays anti-B0 -> D*+ lepton- anti-lepton-neutrino and anti-B0 -> D+ lepton- anti-lepton-neutrino“, Physics Letters B395, 373 (1997) Only 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… 
  • D. Buskulic et al. [ALEPH Collaboration], “A measurement of form factors and |Vcb| from in B0 D*+lnu”  Physics Letters B 359, 236 (1995)  First such measurement at LEP. 
  • D. Buskulic et al. [ALEPH Collaboration], “Measurement of the b hadron lifetime in the J/ψ channel at ALEPH“, Physics Letters B295, 396 (1992). 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.

Book edition

  • “Artificial Intelligence for High Energy Physics“, World Scientific Publishing, edited by Paolo Calafiura, David Rousseau and Kazuhiro Terao, doi:10.1142/12200 March 2022, We’ve collected 20 chapters from the best experts of this now established field.
  • “Proceedings, Connecting The Dots / Intelligent Tracker (CTD/WIT 2017) : Orsay, France, March 6-9, 2017,” edited by C. Germain, H. Grasland, A. Lounis, D. Rousseau and D. Varouchas,  EPJ Web Conf. 150 (2017)

  • “Proceedings, NIPS 2014 Workshop on High-energy Physics and Machine Learning, 13 December 2014, Montreal, Canada”, edited par G. Cowan, B. Kégl, C. Germain, I. Guyon et D. Rousseau, Proceedings of Machine Learning Research 42

     

Datasets

Thesis

David Rousseau. “Mesure de la duree de vie des hadrons B dans le canal de desintegration en J/Ψ à l’experience ALEPH au LEP” Université de la Méditerranée – Aix-Marseille II, 1992. in french. ⟨in2p3-00010922⟩ (document unavailable in electronic form)

Habilitation à Diriger les Recherches

David Rousseau “Recherche du ≪Petit Higgs≫ et développement des algorithmes de reconstruction dans ATLAS.” Université Paris Sud – Paris XI, 2007. ⟨tel-00529499⟩

Editorial activity

To paraphrase Churchill on democracy, I believe the current peer-review system is the worst system, except for all the others. Many times I’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.

I’m an associated editor for Computing and Software for the Big Science

I have served as a reviewer for: Baylearn, Computer Physics Communication, Computing and Software for the Big Science, Connecting The Dots,  European 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 Physics Review, NeurIPS Competition, Nuclear Instruments and Methods A, Physics Review D, Physics Review Letters, Teaching Machine Learning ( ECML ).

I have served as an expert for grant reviews in Belgium, ERC, France, Germany, Portugal, Switzerland and UK.

All articles

As a particle physicist, I have about 1300  publications through my experiments ALEPH (1991-2000) and ATLAS (1998-).  This 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.

All my publications in inspireHep (no false positive I’m aware of, a few non-HEP publications missing). All my publications in inspireHep excluding the ones from ALEPH and ATLAS.

All my publications in HAL (completeness not guaranteed and a number of false hits due to homonyms)

All my publications on Researchgate and All my publications on Google Scholar are not curated.