Introduction

I’m a machine-learning researcher specialising in computer vision, with a focus on applications in neutrino physics. I began my journey in 2018 and carried out my PhD research at CERN. Since 2021, I’ve been a member of the Rubbia group at ETH Zurich. My work primarily involves event reconstruction in neutrino experiments. I have contributed to DUNE in the United States and am currently involved in T2K and Hyper-K in Japan, as well as FASER at CERN. Within T2K, I serve as the convener for machine-learning activities for the near detector. I have presented my research at international conferences and published in respected journals, particularly on applying deep learning to neutrino analysis.

I also teach AI applications in neutrino physics and have previous teaching experience in Computer Architecture, Operating System Design, and Distributed Systems.

Neutrino event display

Experience

Selected publications

  • "An ultrafast plenoptic-camera system for high-resolution 3D particle tracking in unsegmented scintillators" Till Dieminger, Saúl Alonso-Monsalve, Christoph Alt, Claudio Bruschini, Noemi Bührer, Edoardo Charbon, Kodai Kaneyasu, Tim Weber, Davide Sgalaberna, Nature Communications, 2026.
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  • "Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training" Saúl Alonso-Monsalve, Fabio Cufino, Umut Kose, Anna Mascellani, André Rubbia, arXiv pre-print, 2026.
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  • "Transformer-Based Pulse Shape Discrimination in HPGe Detectors with Masked Autoencoder Pre-training" Marta Babicz, Saúl Alonso-Monsalve, Alain Fauquex, Laura Baudis, arXiv pre-print, 2026.
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  • "Contrastive learning for robust representations of neutrino data" Alex Wilkinson, Radi Radev, Saúl Alonso-Monsalve, Physical Review D, 2025.
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  • "AI-based particle track identification in scintillating fibres read out with imaging sensors" Noemi Bührer, Saúl Alonso-Monsalve, Matthew Franks, Till Dieminger, Davide Sgalaberna, Journal of Instrumentation, 2025.
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  • "Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography" Saúl Alonso-Monsalve, Leigh H. Whitehead, Adam Aurisano, Lorena Escudero Sanchez, arXiv pre-print, 2025.
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  • "Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of simulated neutrino interactions" Saúl Alonso-Monsalve, Davide Sgalaberna, Xingyu Zhao, Adrien Molines, Clark McGrew, André Rubbia, Communications Physics, 2024.
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  • "Artificial intelligence for improved fitting of trajectories of elementary particles in dense materials immersed in a magnetic field" Saúl Alonso-Monsalve, Davide Sgalaberna, Xingyu Zhao, Clark McGrew, André Rubbia, Communications Physics, 2023.
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  • "Adversarial methods to reduce simulation bias in neutrino interaction event filtering at liquid argon time projection chambers" Marta Babicz, Saúl Alonso-Monsalve, Stephen Dolan, Kazuhiro Terao, Physical Review D, 2022.
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  • "Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors" Saúl Alonso-Monsalve, Dana Douqa, César Jesús-Valls, Thorsten Lux, Sebastian Pina-Otey, Federico Sánchez, Davide Sgalaberna, Leigh H. Whitehead, Physical Review D, 2021.
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  • "Neutrino interaction classification with a convolutional neural network in the DUNE far detector" B. Abi et al. (DUNE Collaboration), Physical Review D, 2020.
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  • "Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks" Saúl Alonso-Monsalve, Leigh H. Whitehead, IEEE Transactions on Neural Networks and Learning Systems, 2020.
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  • "Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators" Saúl Alonso-Monsalve, Andrés Suárez-Cetrulo, Alejandro Cervantes, David Quintana, Expert Systems with Applications, 2020.
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For a comprehensive list of publications, please refer to the following profiles.

Google Scholar ResearchGate ORCID INSPIRE HEP

Invited talks

  • "Deep learning for neutrino physics" ( LONDON, ENGLAND) Plenary talk, NuPhys 2026: Prospects in Neutrino Physics, King's College London, January 7-9, 2026.
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  • "Algorithmic View on Deep Learning for Accelerator Neutrino Experiments" ( ZURICH, SWITZERLAND) Plenary talk, ZPW2026: Machine Learning Techniques for Particle Physics, University of Zurich, January 5-7, 2026.
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  • "Pushing the boundaries of neutrino physics with deep learning" ( CALIFORNIA, USA) Seminar, SLAC National Accelerator Laboratory, Stanford University, May 8, 2025.
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  • "Machine learning methods for event reconstruction in accelerator neutrino experiments" ( VENDÉE, FRANCE) Conference talk, 5th World Summit on Exploring the Dark Side of the Universe (EDSU2024), Île de Noirmoutier, Jun 2-7, 2024.
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  • "Artificial intelligence and machine learning in neutrino physics" ( NAPLES, ITALY) Conference talk, 22nd International Workshop on Next Generation Nucleon Decay and Neutrino Detectors (NNN23), Procida, Oct 11-13, 2023.
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  • "Computer vision introduction" ( CALIFORNIA, USA) Lecture, SLAC Summer Institute, SLAC National Accelerator Laboratory, Stanford University, Aug 7-18, 2023.
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  • "Machine learning for neutrino experiments" ( TOKYO, JAPAN) Seminar, Kavli Institute for the Physics and Mathematics of the Universe (IPMU), University of Tokyo, Jul 6, 2023.
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  • "Machine learning and high-performance computing for neutrino oscillations" ( NICOSIA, CYPRUS) Seminar, EuroHPC National Competence Centre, The Cyprus Institute, Oct 18, 2022.
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  • "Deep learning in neutrino experiments" ( ZURICH, SWITZERLAND) Colloquium, Institute for Particle Physics and Astrophysics, ETH Zurich, Nov 3, 2020.
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  • "Machine learning in neutrino experiments" ( SHEFFIELD, ENGLAND) Seminar, Department of Particle Physics and Particle Astrophysics, University of Sheffield, Nov 18, 2019.
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  • "Machine learning in neutrino experiments" ( NEW YORK, USA) Seminar, Department of Physics and Astronomy, Stony Brook University, Oct 28, 2019.
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  • "Machine learning for neutrino identification" ( MEXICO CITY, MEXICO) Plenary conference talk, Symposium on Artificial Intelligence for Science, Industry and Society, UNAM, Oct. 21-25, 2019.
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Public outreach talk at T3chFest (in Spanish)

Events organised

Highlighted awards and grants

  • Swiss AI Initiative grant (Principal investigator) Swiss AI: 50,000 GPU compute-node hours for the project "Scalable and Transferable AI for Neutrino Physics", 2026.
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  • AI for Science (AI4Sci@NERSC) grant (Principal investigator) NERSC (Lawrence Berkeley National Laboratory, CA, USA): 5,000 GPU compute-node hours for the project "Deep Learning for Neutrino Event Reconstruction", 2025–2026.
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  • Swiss AI Initiative grant (Principal investigator) Swiss AI: 30,000 GPU compute-node hours for the project "Artificial Intelligence for Neutrino Physics", 2025.
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  • HDR ML Challenge National Science Foundation: 3rd place among 275 participants (2,478 submissions) in the Anomaly Detection Challenge on LIGO O3 gravitational-wave data (nsfhdr.org/mlchallenge), 2025.
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  • Enrique Fuentes Quintana award Funcas Organization, for the best Spanish doctoral thesis within the fields of Engineering, Mathematics, Physics and Architecture, Dec 2022.
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  • Spanish Computer Science Society (SCIE) award BBVA Foundation, for young computer science researchers, for the distinguishing, innovative, and relevant doctoral work, Nov 2022.
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  • Margarita Salas award Madrid City Council, accessit for one of the best doctoral theses in the field of basic sciences, Nov 2022.
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  • Outstanding thesis award Universidad Carlos III de Madrid: for the unique merits and values of the doctoral thesis, 2021.
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  • Excellence award Social Council, Universidad Carlos III de Madrid, Former students (Alumni): for the excellent professional development carried out between 3 and 5 years after graduation, 2021.
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  • National parallel programming competition Sociedad Arquitectura y Tecnología de Computadores, Jornadas Sarteco (programming tasks using OpenMP, MPI, and CUDA), 2nd place (Málaga 2017), 3rd place (Salamanca 2016).
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  • Programming competition T3chFest UC3M, 1st place (algorithmics using Python), Madrid, Feb 2016.
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