Papers

  • E. Moreno et al., COLLIDE-2V - 750 Million Dual-View LHC Event Dataset for Low-Latency ML

    Proceedings of 28th Conference on Computing in High Energy and Nuclear Physics (CHEP 2026), Bangkok, Thailand (2026).

  • T. Ourida at al., Real-Time Uncertainty Quantification for Jet Tagging Models on the Level-1 Trigger
  • T. Ourida at al., Attention-Driven Context Enrichment for Stabilized Real-Time Jet Tagging

    Proceedings of 28th Conference on Computing in High Energy and Nuclear Physics (CHEP 2026), Bangkok, Thailand (2026).

  • J. Gonski et al., Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

    White paper from Machine Learning for the Front End (ML4FE) Workshop (2025), submitted to PRX Intelligence.

  • J.F. Schulte et al., hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware, accepted by ACM Transactions on Reconfigurable Technology and Systems.

  • Summers et al., Roadmap on fast machine learning for science, Mach. Learn.: Sci. Technol. 7 (2026) 021501.
  • M.Barbone et al., Application of computational modelling to particle physics, Comput. Phys. Commun. Vol. 37 (2025), Iss. 5 : pp. 1358–1382.
  • Z. Zheng et al., JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers
  • L. Laatu et al., Sub-microsecond Transformers for Jet Tagging on FPGAs Machine Learning and the Physical Sciences Workshop, NeurIPS (2025).
  • Z.Que et al., JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs.2025 International Conference on Field Programmable Technology (ICFPT), Shanghai, China
  • P. Odagiu et al., Sets are All You Need: Ultrafast Jet Classification on FPGAs for HL-LHC, Machine Learning: Science and Technology (2024)
  • C. Brown et al., , Front. Artif. Intell. 7:1339785 (2024)
  • M. Mieskolainen, , EPJ Web of Conferences 295, 09021 (2024)
  • M. Barbone et al., , EPJ Web of Conferences 295, 11010 (2024)
  • M. Barbone et al., , EPJ Web of Conferences 295, 09014 (2024)
  • C. Brown et al., , EPJ Web of Conferences 295, 09037 (2024)
  • M. Barbone et al., , EPJ Web of Conferences 295, 09002 (2024)
  • F. Wojcicki et al., , 2022 International Conference on Field-Programmable Technology (ICFPT).
  • M. Barbone et al., GPU acceleration of Monte Carlo simulations: particle physics methods applied to medicine, ACAT 2022 Conference Proceedings.
  • L. Borgna et al., Accelerating the DBSCAN clustering algorithm for low-latency primary vertex reconstruction, ACAT 2022 Conference Proceedings.
  • Z. Que et al., , ACM Trans. Embed. Comput. Syst. Vol. 23 Article 17 (2024)
  • Z. Que et al., , 32nd International Conference on Field-Programmable Logic and Applications (2022)
  • L. Våge, Accelerated graph building for particle tracking graph neural nets, CTD 2022 Conference Proceedings.
  • C. Brown et al., , CTD 2022 Conference Proceedings.
  • C. Brown et al., , J. Phys.: Conf. Ser. 2438 012106 (2023)
  • M. Barbone et al., ,J. Phys.: Conf. Ser. 2438 012023 (2023)

    Talks & posters

    • E. Moreno, COLLIDE-2V - 750 Million Dual-View LHC Event Dataset for Low-Latency ML, CHEP 2026
    • T. Ourida Real-Time Uncertainty Quantification for Jet Tagging Models on the Level-1 Trigger, CHEP 2026
    • T. Ourida Attention-Driven Context Enrichment for Stabilized Real-Time Jet Tagging, CHEP 2026
    • L. Laatu, Sub-microsecond Transformers for Jet Tagging on FPGAs, NeurIPS (2025).
    • Z. Que, JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs, ICFPT 2025
    • L. Laatu, Low-latency Jet Tagging for HL-LHC Using Transformer Architectures, FastML 2025
    • C. Sun, Low-Latency Resource-Efficient GNNs for Jet Tagging on FPGAs, FastML 2025
    • L. Laatu, Evolution of the oneAPI backend for hls4ml, FastML 2025
    • C. Brown, CHEP 2023.
    • M. Barbone, Fast, CHEP 2023.
    • M. Barbone, CHEP 2023.
    • M. Barbone, CHEP 2023.
    • M. Mieskolainen, CHEP 2023.
    • F. Wojcicki et al., Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments, FPT 2022.
    • M. Barbone et al., , ACAT 2022.
    • L. Borgna et al., , ACAT 2022.
    • C. Brown, , ML@L1 Trigger Workshop at the LPC, 2022.
    • C. Brown et al., , Fast Machine Learning for Science Workshop 2022.
    • Z. Que et al., , Fast Machine Learning for Science Workshop 2022.
    • B. Radburn-Smith et al., , Fast Machine Learning for Science Workshop 2022.
    • Z. Que et al., , Monthly Fast ML meeting.
    • Z. Que et al., Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs, FPL 2022.
    • L. Våge et al., , Connecting The Dots 2022.
    • C. Brown et al., , Connecting The Dots 2022.
    • T. Ourida et al., , 5th Inter-experiment Machine Learning Workshop, CERN, 2022.
    • A. Rose, , Towards the future of AI, 51³Ô¹ÏÍø, 2022.
    • L. Borgna, , SwiftHEP Workshop 2022.
    • M. Barbone et al., , SwiftHep/ExcaliburHep Workshop, 2021.
    • L. Våge et al., , SwiftHep/ExcaliburHep Workshop 2021.
    • M. Barbone et al., , ACAT 2021.
    • C. Brown et al., , ACAT 2021.
    • M. Barbone, , Geant4 simulation collaboration bi-weekly meeting, 2022.
    • M. Barbone, , HEP Software Foundation Detector Simulation Working Group, 2021.
    • Que et al., JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs.
    • 2025 International Conference on Field Programmable Technology (ICFPT), Shanghai, China

    Code repositories

    • for Centre for Embedded Machine-learning and High-throughput Digital Electronics at 51³Ô¹ÏÍø College
    • repository for HLS-based template for the GNN-based JEDI-net
    • for Multiple Scattering Monte Carlo code

    Seminars & lectures

    • Z. Que, , Compute Accelerator Forum, CERN.
    • M. Barbone, , CERN OpenLab Lecture Programme.
    • A. Rose, , UK Advanced Instrumentation Training 2022.
    • M. Barbone, , Compute Accelerator Forum, CERN.
    • M. Barbone, Practical HPC, Flatiron Institute, New York.

    Masters projects

    • L. Rozanov, Fast Machine Learning for the CMS Level-1 Trigger, MSc thesis (Supervisor A. Tapper)
    • S. Baccas, Accelerated Bayesian Cluster Analysis for Super Resolved Microscopy, (supervisors: A. Rose, P. French).
    • Heterogeneous Hardware Solutions of neutrino algorithms (supervisors: E. Atkin, I. Xiotidis)
      • Track reconstruction of neutrino interactions within a High-Pressure Gas Argon TPC detector
      • Vertex finding in neutrino interactions in a High-Pressure Gas Argon TPC environment with CNNs
    • Optimisation of spline evaluation for neutrino oscillation analysis with Intel OneAPI (supervisors: E. Atkin, I. Xiotidis)
    • Tracking with Quantum Computers in High Energy Physics (supervisors: C. Brown, I. Xiotidis)
    • Quantum Machine Learning for High Energy Physics (supervisor: B. Maier)
    • Using Differentiable Programming for Experiment Optimization (supervisor: B. Maier)
    • Machine learning-based Event Reconstruction for Future Highly Granular Detectors at the Large Hadron Collider (supervisors: R. Bainbridge, B. Maier)
    • Computing students at undergraduate and MSc level through the p (supervisor: W Luk)

     

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