"The pursuit of science seems to me to
require particular courage.
It is concerned with knowledge, achieved through doubt.
Making knowledge about everything available for everybody,
science strives to make sceptics of them all."
Bertolt Brecht, Life of Galileo.
"Non credo che la pratica della scienza possa andar
disgiunta dal coraggio.
Essa tratta il sapere, che è un prodotto
del dubbio; e col procacciare sapere a tutti su ogni cosa, tende a destare
il dubbio in tutti."
Bertolt Brecht, Vita di Galileo.
Universita` di Pisa - Dipartimento di Informatica
Largo B. Pontecorvo 3, 56127 Pisa, ITALY
Phone: +39-050-2212798 - Fax: +39-050-2212726
Room 358 / DN
- Machine Learning, Artificial and Computational Intelligence, Soft Computing
- Learning in Structured Domains, Relational Learning, Relational Data Mining
- Supervised and Unsupervised Neural Networks, Recurrent and Recursive Neural Networks
- Deep Learning, Deep Recurrent Networks, Deep Reservoir Computing, DeepESN
- Processing of Sequences and Structured Information (trees and graphs) in Machine Learning:
Recursive Models, Reservoir Computing, Hidden Markov Models, Kernel-based methods for Non-vectorial Data
- Applications to Cheminformatics, QSPR/QSAR Analysis, and Toxicity predictions
- Applications to Bioinformatics and Health/biomedical Informatics
- Learning in Robotics and Wireless/Intelligent Sensor Networks
- Human Activity Recognition for Ambient Assisted Living
- Pioneering since the 90’s the development of new models for learning structured and graph data
(see also Methodologies and Models in CIML for
Recursive and Contextual Neural Networks topic)
- introducing the first (constructive) model of Neural Network for Graphs in the class of deep spatial convolution approaches [2005-2009] (see IEEE TNN 2009 and the references therein)
- introducing a deep architecture for the Reservoir Computing for sequences (Neurocomputing 2017, Neural Networks 2018),
trees (Information Science 2019), and graphs ( AAAI 2020)
- providing a survey for the Deep Graph Networks (Neural Networks 2020)
Leading since 2008 the CIML
group for basic and applied research on Machine Learning for complex data.
Editorial and Program Committees
Editorial Committes 2013-2020:
- Neurocomputing - Elsevier (ISSN: 0925-2312), special issue ESANN 2012 04/2012
- Intelligenza Artificiale, IOS Press, ISSN print 1724-8035, ISSN online 2211-0097
- Cognitive Computation - Elsevier, guest editor special issue on “Trends in Reservoir Computing”, 2020
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS) ISSN: 2162-237X (since Jan. 2019)
- Gest editor for Special issues on IEEE TNNLS:
PC Conferences 2013-2022:
- IJCAI-ECAI 2022 Senior program committee
- IJCAI (International Joint Conference on Artificial Intelligence)
- ECAI (European Conference on Artificial Intelligence)
- IEEE SCCI CIDM (IEEE Symposium on Computational Intelligence and Data Mining)
- IEEE SCCI DL (IEEE Symposium on Deep Learning)
- IEEE IJCNN (International Joint Conference on Neural Networks)
- ESANN (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning)
- IEEE INISTA (IEEE International Symposium on INnovations in Intelligent SysTems and Applications)
- ICANN (International Conference on Artificial Neural Networks)
- ICPRAM (International Conference on Pattern Recognition Applications and Methods)
- IEEE ETFA (Emerging Technologies and Factory Automation, Track 10 PC)
- NC2 (Workshop on New Challenges in Neural Computation and Machine Learning)
- MOD (International Workshop on Machine Learning, Optimization and Big Data)
- IML (International Conference on Internet of Things and Machine Learning)
- AI*IA (Conference of the Italian Association for Artificial Intelligence)
- WIRN (Italian Workshop on Neural Networks)
- Also involved as reviewer for NeurIPS 2019 (best reviewers list) & 2020, AAAI 2020, ICML 2021
Organizer of the
See also (for full references):
- Sono disponibili Proposte di Tesi sul Machine Learning: contattare all'indirizzo email@example.com, tag: [Richiesta-Tesi]
- EU H2020 TEACHING : A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence [2020-2022]
- EU H2020 TAILOR: Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization [2020-2023]
- BrAID: Brugada syndrome and Artificial Intelligence applications to Diagnosis (Bando Ricerca e Salute 2018 - Regione Toscana) [2020-2023] (Unit leader)
- EU FP7 RUBICON: Robotics UBIquitous COgnitive Network (WP leader)
- EU FP7 DOREMI:
Decrease of cOgnitive decline, malnutRition and sedEntariness by elderly
empowerment in lifestyle Management and social Inclusion (WP leader)
- ASPIS: Early seismic alert for sensitive infrastructures (POR FSE 2014-2020) (Unit leader)
- International Project: "Adaptive Processing of Data Structures" (Australian Research Council)
- National Project: "Design of biologically active molecules by neural network techniques applied to QSAR studies and by investigation of ligand-macromolecule interactions".
("Progettazione di molecole biologicamente attive mediante studi QSAR con tecniche neurali e studi di interazione ligando-macromolecola") ( Cofin 99 - MURST and University of Pisa)
- National Projects PRIN - MIUR: Cofin 1999, Cofin 2000, Cofin 2001, Cofin 2002, Cofin 2003, Cofin 2005.