Antonio Pietrabissa
(updated: 31/10/2022)
Antonio Pietrabissa is Associate Professor at the Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) of the University of Rome Sapienza, where he received his degree in Electronics Engineering and his PhD degree in Systems Engineering in 2000 and 2004, respectively, and where, since 2010, he has been teaching Automatic Control and Process Automation. He holds the National Scientific qualification (ASN) as Full Professor for the disciplinary field of 09/G1 - Systems and control engineering. His main research focus is on the application of control theory to the networks by means of internal model control, Markov decision processes, (deep) reinforcement learning, robust control, non-cooperative control.
Currently, he serves as Associate Editor for Control. Eng. Pract. (Elsevier) and for IEEE Trans. Autom. Sci. Eng.
- Scopus: h-index: 20; citations: 1212.
- Google Scholar: h-index: 23; quotes: 1695; i-10 index: 55.
- FedMedAI, on medical applications of federated learning (https://sites.google.com/diag.uniroma1.it/fedmedai/home), funded by Regione Lazio (IT).
Networked Systems
Time-delay systems
Non-cooperative control
Robust control
Markov Decision Processes
Reinforcement Learning
Deep Reinforcement Learning
Membro di
Awards
Ultime pubblicazioni
| A Load Balancing Algorithm for Equalising Latency across Fog or Edge Computing Nodes on IEEE TRANSACTIONS ON SERVICES COMPUTING | 2023 |
| Behavioural Cloning for Serious Games in Support of Pediatric Neurorehabilitation on 2023 31st Mediterranean Conference on Control and Automation (MED) - Proceedings | 2023 |
| CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence on HEALTHCARE | 2023 |
| Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension on HEALTHCARE | 2023 |
| 5G-ALLSTAR: Beyond 5G Satellite-Terrestrial Multi-Connectivity on 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) | 2022 |
| A Weighted Average Consensus Approach for Decentralized Federated Learning on MACHINE INTELLIGENCE RESEARCH | 2022 |
| An Adaptive Model Averaging Procedure for Federated Learning (AdaFed) on JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY | 2022 |