Francesco Piccialli is Full Professor of Computer Science and Artificial Intelligence at the Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Italy. He received his Laurea degree in Computer Science and his Ph.D. in Computational and Computer Sciences from the University of Naples Federico II.

He is head and co-founder of the M.O.D.A.L. - Mathematical mOdelling and Data AnaLysis research group and has been a research fellow of CINI, the Italian National Interuniversity Consortium for Informatics, since 2013. His research focuses on Federated and Distributed Learning, Edge AI, Green and Sustainable AI, Generative and Agentic AI, Digital Twins, Scientific Machine Learning, Trustworthy AI, time-series analysis and predictive intelligence. These methods are applied to smart cities, industrial systems, healthcare, agriculture, geosciences, energy, mobility and drug discovery.

He has authored more than 220 peer-reviewed publications in international journals, conferences and books. He has coordinated and led numerous competitive European, national and industrial research projects, including Horizon Europe MSCA, PRIN, PNRR, Erasmus+, MIMIT and industry-funded initiatives.

He serves in editorial roles for several leading international journals and has received scientific, editorial and conference-leadership awards. Since 2021, he has been included in the Stanford/Elsevier World’s Top 2% Scientists List. He is co-founder and Scientific Lead of the University spin-off PreDICO, focused on predictive analytics and intelligence, and is co-inventor of a granted Italian utility-model patent for a prediction system.

His international activities include research collaborations with universities and laboratories in Europe, Asia, Australia and the United States, service as a reviewer and evaluator of doctoral theses and research proposals, and visiting appointments including The Hong Kong Polytechnic University.

My research develops methodological and applied Artificial Intelligence solutions for distributed, data-intensive and dynamic environments. The main research lines are:

• Federated and Distributed Learning: aggregation strategies, non-IID data, robustness, privacy, security, graph federated learning and distributed foundation models.
• Edge, Lightweight and Green AI: efficient learning and inference on constrained IoT devices, energy-aware client selection, sustainable AI and Edge–Cloud–HPC computing.
• Generative and Agentic AI: generative models, synthetic data, large language models, autonomous agents and multi-agent systems.
• Digital Twins and Predictive Intelligence: adaptive digital twins, time-series forecasting, multimodal data fusion, predictive maintenance and what-if analysis.
• Scientific Machine Learning: physics-informed neural networks, data-driven modelling of complex systems and AI methods for mathematics, engineering and geosciences.
• Trustworthy and Explainable AI: interpretability, robustness, uncertainty, privacy-by-design and transparent AI systems.

The research is carried out through national and international collaborations with universities, research centres and companies. A central objective is to combine methodological rigour with measurable real-world impact, transferring results to industry and society through open scientific outputs, prototypes, patents, funded projects and the PreDICO academic spin-off.

Research topics at M.O.D.A.L. Federated Learning Scientific Machine Learning

Ongoing and Recent Projects

• FLINT – Federated Learning for INdustrial Twins — Principal Investigator. MIMIT / STEP, 36 months, started in March 2026. Federated and Edge AI architectures integrated with Digital Twins for privacy-preserving, real-time industrial intelligence.
• ECHO-TWIN – Edge-Cloud-HPC Optimised Twins — Funded by the PNRIC 2021–2027 programme, 18 months, started on May 5, 2026. An Edge–Cloud–HPC ecosystem for Digital Twin-enabled applications in health, climate, mobility and AI.
• TUAI – Towards an Understanding of Artificial Intelligence via a Transparent, Open & Explainable Perspective — Scientific Lead for UNINA. Horizon Europe MSCA Doctoral Network, Project No. 101168344, October 2024–September 2028.
• ML-PO – Machine-Learning-aided 13C NMR/CEF rapid analysis of polyolefin materials — Scientific Lead. Dutch Polymer Institute, May 2024–October 2026.

Selected Completed Projects

• FAIR – Future Artificial Intelligence Research — Work Package co-Leader at UNINA, Spoke 3 “Resilient AI”, completed in March 2026.
• AIFEMO – AI-Optimized Fuel Efficiency in Maritime Operations — Principal Investigator. Innovation Grant of the National Centre for HPC, Big Data and Quantum Computing, with Fincantieri; completed in March 2026.
• G.A.N.D.A.L.F. – GAN Approaches for Non-IID Aiding Learning in Federations — National Principal Investigator, PRIN 2022.
• D.I.R.E.C.T.I.O.N.S. – Deep learning aIded foReshock deteCTIOn Of iNduced mainShocks — UNINA Research Unit Coordinator, PRIN-PNRR 2022.
• C.L.A.I.M. – Artificial Intelligence for Competences and Learning — Principal Investigator, Erasmus+ KA220-VET.
• SESG – Integrated Platform for Enhanced Analysis of Environmental, Social and Governance Reports — UNINA Research Unit Scientific Lead, Innovation Grant.
• ELIXIR x NextGenerationIT — UNINA Research Unit Scientific Lead, PNRR research infrastructure programme.
• NOACRONYM+ / PredicTS — UNINA Research Unit Scientific Lead, proof-of-concept programme for predictive time-series technologies.
• 4I – Mixed Reality, Machine Learning, Gamification and Education for Industry — Principal Investigator, MISE / PON I&C.
• C.E.T.R.A. – Cultural Equipment with Transmedial Recommendation Analytics and A.M.I. – Advanced Modalities of Interactions — Principal Investigator.

By October 2025, the competitive projects led as Principal Investigator, Scientific Lead or Research Unit Coordinator accounted for more than €6.9 million in managed project funding. This portfolio has since expanded with new initiatives including FLINT and ECHO-TWIN.

View the full and updated project portfolio on the M.O.D.A.L. website

Research profile. More than 220 peer-reviewed publications. Metrics reported on 31 October 2025: Google Scholar h-index 51 and 10,719 citations; Scopus h-index 44 and 7,294 citations.

Recent Selected Publications

• 2026 — M. Savoia, C. Della Bruna, C. D’Addio, F. Piccialli, “SAGE: Sustainable and green energy-aware client selection for federated learning in IoT,” Applied Energy, 410, 127568. DOI
• 2026, accepted — L. Qiu, S. Izzo, D. Annunziata, F. Giampaolo, F. Piccialli, “FLIP4Tab: Federated Latent In-context Priors for Tabular Data Synthesis,” Main Research Track, ECML PKDD 2026. Announcement
• 2025 — F. Piccialli et al., “A digital twin framework for urban parking management and mobility forecasting,” Nature Communications, 16, 9400. DOI
• 2025 — F. Piccialli et al., “AgentAI: A Comprehensive Survey on Autonomous Agents in Distributed AI for Industry 4.0,” Expert Systems with Applications, 291, 128404. DOI
• 2025 — F. Piccialli, C. Della Bruna, D. Chiaro, P. Qi, M. Savoia, “AGRIFOLD: AGRIculture Federated learning for Optimized Leaf disease Detection,” Expert Systems with Applications, 289, 128371. DOI
• 2025 — F. Piccialli, D. Chiaro, P. Qi, V. Bellandi, E. Damiani, “Federated and edge learning for large language models,” Information Fusion, 117, 102840. DOI
• 2025 — P. Qi, D. Chiaro, F. Piccialli, “Small models, big impact: A review on the power of lightweight Federated Learning,” Future Generation Computer Systems, 162, 107484. DOI
• 2025 — D. Thakur, A. Guzzo, G. Fortino, F. Piccialli, “Green Federated Learning: A new era of Green Aware AI,” ACM Computing Surveys, 57(8), 1–36. DOI
• 2025 — D. Chiaro, P. Qi, E. Prezioso, A. Guzzo, F. Piccialli, “FLAME: Federated Learning for Attack Mitigation and Evasion,” IEEE IPDPS, 605–615. DOI
• 2025 — D. Chiaro, P. Qi, V. Mele, F. Piccialli, “FLAIR: Federated Learning for Augmented Industrial Retrieval,” IEEE Internet of Things Journal, 12(19), 39338–39345. DOI
• 2024 — F. Piccialli, M. Canzaniello, D. Chiaro, S. Izzo, P. Qi, “GRAPHITE: Generative Reasoning and Analysis for Predictive Handling in Traffic Efficiency,” Information Fusion, 106, 102265. DOI
• 2024 — P. Qi, D. Chiaro, F. Giampaolo, F. Piccialli, “KAFÈ: Kernel Aggregation for FEderated,” ECML PKDD, LNCS 14944, 56–71. DOI
• 2024 — P. Qi, D. Chiaro, A. Guzzo, M. Ianni, G. Fortino, F. Piccialli, “Model aggregation techniques in federated learning: A comprehensive survey,” Future Generation Computer Systems, 150, 272–293. DOI
• 2024 — V. Romanelli et al., “Enhancing De Novo Drug Design across Multiple Therapeutic Targets with CVAE Generative Models,” ACS Omega, 9(43), 43963–43976. DOI
• 2024 — V. Convertito, F. Giampaolo, O. Amoroso, F. Piccialli, “Deep learning forecasting of large induced earthquakes via precursory signal,” Scientific Reports, 14. DOI

Academic Profiles

Google Scholar ORCID DBLP Scopus ResearchGate

Current Editorial Appointments

• Associate Editor, Journal of Scientific Computing, Springer (since 2026).
• Associate Editor, Neurocomputing, Elsevier (since 2026).
• Associate Editor, IEEE Journal of Biomedical and Health Informatics (since 2025).
• Associate Editor for Europe, Neural Computing and Applications, Springer (since 2022).
• Associate Editor, IEEE Transactions on Artificial Intelligence (since 2022).
• Associate Editor, IEEE Transactions on Industrial Informatics (since 2019).
• Associate Editor and Area 4 Editor, IEEE Internet of Things Journal (since 2019).
• Editorial Board Member, Future Generation Computer Systems, Elsevier (since 2017).
• Editorial Board Member, Information Fusion, Elsevier (since 2021).
• Editorial Board Member, Scientific Reports, Nature Portfolio (since 2023).
• Editorial Board Member, Communications on Applied Mathematics and Computation, Springer (since 2024).

Conference and Scientific Leadership

• General Chair, 2nd International Conference on Federated Learning and Intelligent Computing Systems (FLICS 2026), Valencia, Spain.
• General Chair, International Conference on Machine Learning and Intelligent Systems Engineering (MLISE 2026).
• Organiser, International Summer School on Generative AI, Sapienza University of Rome, June 2026.
• Organiser, FLUID Workshop at AAAI 2025, and co-organiser of FIDTA 2025 at ACM MobiHoc.
• Area Chair, ACM Multimedia (2022, 2024 and 2025 editions).
• Selected Program Committee service for RTSS, ECML PKDD, ICDCS, ICWS, ISWC, ESWC, SAC and other major international conferences.

Selected Awards and Recognition

• Editor’s Choice Paper, Future Generation Computer Systems, 2026.
• Editorial Contribution Award, Neural Computing and Applications, Springer Nature, 2025.
• Outstanding Associate Editor, IEEE Transactions on Industrial Informatics, 2024.
• Three international Best Paper Awards, including the IEEE ICDM workshop award for “Cut the Peaches” in 2022.
• Included in the Stanford/Elsevier World’s Top 2% Scientists List since 2021.
• IEEE conference Outstanding Leadership Awards for scientific and organisational service.

Technology Transfer and International Service

• Co-founder and Scientific Lead of PreDICO, an academic spin-off and innovative start-up of the University of Naples Federico II.
• Co-inventor of the Italian utility-model patent “Sistema di Predizione”, granted on 22 August 2024.
• Visiting Professor at The Hong Kong Polytechnic University, November 2025.
• Opponent and international doctoral-thesis evaluator at universities in Europe, Asia and Australia.
• Reviewer of competitive research proposals for national and international funding organisations, including ERC-related, Dutch, Austrian and Swiss programmes.
• Member of IEEE and ACM.

Courses — Academic Year 2025–2026

• Algorithms and Applications for Artificial Intelligence — 6 ECTS, Master’s Degree in Mathematics.
• Deep Learning — 6 ECTS, Master’s Degree in Mathematical Engineering.
• Programming Laboratory — 9 ECTS, Bachelor’s Degree in Mathematics.

Doctoral Teaching

• AI Paradigms for Scientific Machine Learning — 24-hour doctoral course, Ph.D. Programme in Mathematics and Applications, editions 2024 and 2025.
• Member of the Academic Board of the Ph.D. Programme in Mathematics and Applications.

Ph.D. Supervision

Scientific tutor or supervisor of 17 Ph.D. candidates and graduates at the University of Naples Federico II.

Current and recent Ph.D. candidates:

• Martina Savoia, Marzia Canzaniello, Francesco Magliocca and Daniela Annunziata — XXXVIII cycle.
• Sara Amitrano and Sundas Sarwar — XXXIX cycle.
• Anna Borrelli and Valentina De Angelis — XL cycle.
• Lingyu Qiu, Makhmoor Fiza Murk and S. M. Asiful Huda — XLI cycle.

Former Ph.D. students: Vincenzo Schiano di Cola, Fabio Giampaolo, Edoardo Prezioso, Stefano Izzo, Pian Qi and Diletta Chiaro.

Student Mentoring

Supervisor of at least 22 Master’s theses and 2 Bachelor’s theses in Mathematics, Mathematical Engineering and Data Science, mainly on Machine Learning, Deep Learning, Federated Learning, Generative AI, Digital Twins, healthcare, geosciences and predictive analytics.

Institutional teaching page