Research

Papers

• Fast Machine Learning for Science 2023 Roadmap Article, Machine Learning: Science and Technology (2024)
• 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., Quantum Pathways for Charged Track Finding in High-Energy Collisions, Front. Artif. Intell. 7:1339785 (2024)
• M. Mieskolainen, HyperTrack: neural combinatorics for high energy physics, EPJ Web of Conferences 295, 09021 (2024)
• M. Barbone et al., Fast, high-quality pseudo random number generators for heterogeneous computing, EPJ Web of Conferences 295, 11010 (2024)
• M. Barbone et al., Embedded Continual Learning for HEP, EPJ Web of Conferences 295, 09014 (2024)
• C. Brown et al., The Deployment of Realtime ML in Changing Environments, EPJ Web of Conferences 295, 09037 (2024)
• M. Barbone et al., Acceleration of a Deep Neural Network for the Compact Muon Solenoid, EPJ Web of Conferences 295, 09002 (2024)
• F. Wojcicki et al., Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments, 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., LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors, ACM Trans. Embed. Comput. Syst. Vol. 23 Article 17 (2024)
• Z. Que et al., Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs, 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., Track Finding and Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System, CTD 2022 Conference Proceedings.
• C. Brown et al., Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System, J. Phys.: Conf. Ser. 2438 012106 (2023)
• M. Barbone et al., Demonstration of FPGA Acceleration of Monte Carlo Simulation,J. Phys.: Conf. Ser. 2438 012023 (2023)

Talks & posters

• C. Brown, The Deployment of Realtime ML in Changing Environments CHEP 2023.
• M. Barbone, Fast, high-quality pseudo random number generators for heterogeneous computing CHEP 2023.
• M. Barbone, Embedded Continual Learning for HEP CHEP 2023.
• M. Barbone, Acceleration of a CMS DNN based Algorithm CHEP 2023.
• M. Mieskolainen, HyperTrack: neural combinatorics for high energy physics CHEP 2023.
• F. Wojcicki et al., Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments, FPT 2022.
• M. Barbone et al., GPU acceleration of Monte Carlo simulations: particle physics methods applied to medicine, ACAT 2022.
• L. Borgna et al., Accelerating the DBSCAN clustering algorithm for low-latency primary vertex reconstruction, ACAT 2022.
• C. Brown, Development of a DNN for Vertexing and Track to vertex association in the GTT, ML@L1 Trigger Workshop at the LPC, 2022.
• C. Brown et al., End-to-End Vertex Finding for the CMS Level-1 Trigger, Fast Machine Learning for Science Workshop 2022.
• Z. Que et al., Accelerating JEDI-net for jet tagging on FPGAs, Fast Machine Learning for Science Workshop 2022.
• B. Radburn-Smith et al., Deployment of ML in changing environments, Fast Machine Learning for Science Workshop 2022.
• Z. Que et al., Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs, 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., Accelerated graph building for particle tracking graph neural nets, Connecting The Dots 2022.
• C. Brown et al., Track Finding and Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System, Connecting The Dots 2022.
• T. Ourida et al., FPGA acceleration of the CMS DNN based LLP Jet Algorithm for the LHC High-Luminosity upgrade, 5th Inter-experiment Machine Learning Workshop, CERN, 2022.
• A. Rose, Centre for High-Throughput Digital Electronics and Embedded Machine Learning, Towards the future of AI, Imperial College London, 2022.
• L. Borgna, Fast Primary Vertex and Track Reconstruction Methods, SwiftHEP Workshop 2022.
• M. Barbone et al., Simulations: A case study, SwiftHep/ExcaliburHep Workshop, 2021.
• L. Våge et al., Accelerating particle tracking for the HL-LHC, SwiftHep/ExcaliburHep Workshop 2021.
• M. Barbone et al., Demonstration of FPGA Acceleration of Monte Carlo Simulation, ACAT 2021.
• C. Brown et al., Neural network based primary vertex reconstruction with FPGAs for the upgrade of the CMS level-1 trigger system, ACAT 2021.
• M. Barbone, Demonstration of FPGA Acceleration of Monte Carlo Simulation, Geant4 simulation collaboration bi-weekly meeting, 2022.
• M. Barbone, FPGA Acceleration of Monte Carlo Simulation, HEP Software Foundation Detector Simulation Working Group, 2021.

Code repositories

• Github Repository for Centre for Embedded Machine-learning and High-throughput Digital Electronics at Imperial College
• GNN-JEDInet-FPGA repository for HLS-based template for the GNN-based JEDI-net
• Repository for Multiple Scattering Monte Carlo code

Seminars & lectures

• Z. Que, Accelerating graph neural network for jet tagging using FPGAs, Compute Accelerator Forum, CERN.
• M. Barbone, Introduction to FPGA acceleration, CERN OpenLab Lecture Programme.
• A. Rose, Trigger and DAQ, UK Advanced Instrumentation Training 2022.
• M. Barbone, Introduction to FPGA acceleration, Compute Accelerator Forum, CERN.
• M. Barbone, Practical HPC, Flatiron Institute, New York.

Masters projects

• 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 Custom Computing Research Group (supervisor: W Luk)