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Claudio Gallicchio
Claudio Gallicchio
Department of Computer Science - University of Pisa (Italy)
Ph.D. in Computer Science
nihil difficile volenti
Contacts
Department of Computer Science, University of Pisa.
Largo Bruno Pontecorvo, 3 - 56127 Pisa, Italy
I am an Associate Professor at the Department of Computer Science, University of Pisa, where I lead the research line on Neural Dynamics and Neuromorphic AI within the Computational Intelligence & Machine Learning Group (CIML). I am Principal Investigator of the University of Pisa units of the Horizon Europe projects MINDnet and ADA-COLLAB, and I supervise a group of PhD students, post-doctoral researchers and research fellows working on dynamical neural models, reservoir computing and neuromorphic computing for AI.
Research group
The group I lead at the Department of Computer Science works on recurrent and dynamical neural models, reservoir computing, deep learning for graphs, stable neural architectures, learning beyond backpropagation, and neuromorphic hardware for AI.
Adam C. Maćkowiak Pellón — PhD student (from Nov 2026), MSCA MINDnet DC11, hardware-friendly reservoir computing
Giacomo Lagomarsini - PhD student (from Nov 2025), Reservoir Computing
Matteo Pinna - Research fellow (Jul 2025 - Jan 2026), Reservoir Computing and Neuromorphic friendly neural architectures
Funded Projects
PRINCIPAL INVESTIGATOR MINDnet — Neuromorphic Computing and Signal Processing Training Network. Principal Investigator and scientific coordinator of the University of Pisa unit. Horizon Europe, MSCA Doctoral Network, HORIZON-MSCA-2024-DN-01, Grant Agreement 101226674. 2026–2029. Total budget €4,664,170; UniPi unit €281,755. https://cordis.europa.eu/project/id/101226674
ADA-COLLAB — Dependable, autonomous and collaborative artificial intelligence. Principal Investigator of the University of Pisa unit. Horizon Europe. 2026–
NEURONE — Extremely efficient NEUromorphic Reservoir cOmputing in Nanowire network hardwarE. Principal Investigator and Project Coordinator. Italian Ministry of University and Research, PRIN 2022, project code 20229JRTZA, CUP I53D23003600006. 2023–2025. Total budget €252,048. Partners: University of Pisa (coordinator), Politecnico di Torino, INRiM. https://sites.google.com/unipi.it/neurone/home-page Industrial research contracts
Principal Investigator of five industry-funded research contracts at the University of Pisa: Airbus Defence and Space (two projects), Danieli Automation SpA, Danieli & C. Officine Meccaniche SpA, Silvretta Research Inc. (USA).
Work package Leader
EMERGE — Emergent awareness from minimal collectives. Leader of Work Package 4, "Learning and Evolutionary Awareness". European Innovation Council, EIC Pathfinder, Grant Agreement 101070918. 2022–2026. Coordinated by the University of Pisa. https://eic-emerge.eu RELAY — Relational Deep Learning for Energy Analytics. Leader of Work Package 2, "Randomized networks to handle big data". Research Council of Norway (FRIPRO), Grant Agreement 345017. 2024–2027.
TEACHING — A Computing Toolkit for Building Efficient Autonomous Applications Leveraging Humanistic Intelligence. Leader of Work Package 4, "AI as a Service software infrastructure for CPSoS". European Commission, H2020-ICT, Grant Agreement 871385. 2020–2023. Coordinated by the University of Pisa.
Scientific Community Leadership
Founder and Chair, IEEE CIS Task Force on Randomization-Based Neural Networks and Learning Systems (Chair since 2024; Vice-Chair 2020–2024) Founder and Chair (2018–2022), IEEE CIS Task Force on Reservoir Computing Associate Editor, IEEE Transactions on Neural Networks and Learning Systems Member of the faculty board, Italian National PhD Programme in Artificial Intelligence Member of the IEEE CIS Neural Networks Technical Committee and of the IEEE CIS Task Force on Deep Learning
Research - My research interests are at the intersection between the areas of Machine Learning, Deep Learning, Neural Networks, and Dynamical Systems. I currently work on dynamical neural models, reservoir computing, deep learning for graphs, stable neural architectures, and neuromorphic computing.
A short version of my CV is available here.
Students' Supervision - Drop me a line at my email without hesitation.
I am happy to supervise BSc, MSc and PhD students on topics related to
Neuromorphic Computing for AI: using the physical laws and substrates (e.g., photonics, memristors, etc.) to perform neural computation.
Reservoir Computing: randomized recurrent neural networks in which only a small portion of the neural connections need to be trained.
Stable neural architectures: neural networks can be seen as dynamical systems as they unfold in time (e.g., RNNs) or in space (e.g., deep feed-forward architectures); here we design architectures that behave well from the information processing perspective so that gradients and forward computation do not vanish by design.
Beyond backpropagation learning: learning algorithms that are biologically plausible and efficient.
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