Antonio Pietrabissa
(updated: 30/11/2021)
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. 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.
He is the author of more than 50 journal papers and 90 conference papers and book chapters. His scientific activity is characterized by the following bibliometric indices:
- Scopus: h-index: 20; citations: 1086;
- Google Scholar: h-index: 23; quotes: 1468; i-10 index: 47.
He is co-owner of a patent on applications in the field of computer aids for patients with motor disabilities based on model predictive control.
Since 2000, he has participated in 18 research projects funded by the European Union (1 as scientific coordinator of the EU project, 5 as scientific coordinator of the research unit), 4 funded by the European Space Agency (ESA) (as scientific coordinator of the project and of the research unit), 1 funded by the Italian Space Agency (ASI) (as scientific coordinator of the research unit), 3 National projects (2 as scientific coordinator of the project and of the research unit).
Currently, he is the scientific responsible of the following researh projects:
- 5G-ALLSTARS, on 5G communications (https://5g-allstar.eu), funded within the H2020 Europe-South Korea cooperation;
- ARIES, on fire emergency prevention (https://business.esa.int/projects/aries), funded by ESA;
- 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