Research

In this section

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., , 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)

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, ��ý, 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.

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

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 ��ý at undergraduate and MSc level through the p (supervisor: W Luk)