Enrico De Santis
Ph.D in Information and Communication Engineering at Information Engineering , Electronics and Telecommunications department at Sapienza University of Rome.
Artificial Intelligence, Computational Intelligence, Smart Grids, Complex Systems
Scientific Interests & Bio
Enrico De Santis graduated with honors in 2012 in Communication Engineering at University of Rome "Sapienza" and in 2016 he received the Ph.D. in Information and Communication Engineering at Information Engineering, Electronics and Telecommunications department of the same university. During his Ph.D. he spent a long period in Canada and he has been a research assistant at the Computer Science Department of Ryerson University in Toronto with which he currently collaborates.
Research interests of Enrico De Santis are focused on Artificial Intelligence techniques and Complex Systems. His approach is interdisciplinary and his point of view in solving complex problems is based on a strict compromise between the "divide et impera" of the engineering practice and the complex approach offered by the Complexity Theory. The compromise is strengthened by a solid technological and scientific background. Enrico's main research topics focus on prediction and modeling though Soft Computing techniques and Pattern Recognition, such as Clustering, Genetic Algorithms, Fuzzy Logic and Neural Networks.
Currently, as concerns the Data Science and Data Analytics ambits, he is interested in data mining and knowledge extraction to make smart decisions in changing environments. He is working on text mining, advanced statistical techniques and web information processing in collaboration with Eurostat as trainer.
His studies have been applied to Smart Grids field, in collaboration with companies, such as in the electrical power flow management tasks through the Fuzzy Logic - Genetic Algorithms hybrid, in faults modeling, recognition and analysis, in the real-world MV power grid of Rome through evolutionary metric learning techniques.
He is also interested in nature-inspired learning theories and how biological systems, such as brains and silicon-based Information Systems, represent the knowledge.
 E. De Santis, A. Rizzi, A. Sadeghian, and F. Mascioli. Genetic optimization of a fuzzy control system for energy flow
management in micro-grids. In IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, pages
418423, June 2013. doi: 10.1109/IFSA-NAFIPS.2013.6608437.
 E. De Santis, L. Livi, F. Mascioli, A. Sadeghian, and A. Rizzi. Fault recognition in smart grids by a one-class classification
approach. In Neural Networks (IJCNN), 2014 International Joint Conference on, pages 19491956, July 2014. doi:
 E. D. Santis, G. Distante, F. M. F. Mascioli, A. Sadeghian, and A. Rizzi. Evolutionary optimization of a one-class classifica-
tion system for faults recognition in smart grids. In Proceedings of the International Conference on Evolutionary Computa-
tion Theory and Applications (IJCCI 2014), pages 95103, 2014. ISBN 978-989-758-052-9. doi: 10.5220/0005124800950103.
 E. D. Santis, L. Livi, A. Sadeghian, and A. Rizzi. Modeling and recognition of smart grid faults by a combined approach
of dissimilarity learning and one-class classification. Neurocomputing, 170:368 383, 2015. ISSN 0925-2312. doi: http://
 E. De Santis, A. Rizzi, A. Sadeghian, and F. Frattale Mascioli. A learning intelligent system for fault detection in smart
grid by a one-class classification approach. In Neural Networks (IJCNN), 2015 International Joint Conference on, pages
18, July 2015. doi: 10.1109/IJCNN.2015.7280756.
 E. De Santis, F. Mascioli, A. Sadeghian, and A. Rizzi. A dissimilarity learning approach by evolutionary computation
for faults recognition in smart grids. In J. J. Merelo, A. Rosa, J. M. Cadenas, A. Dourado, K. Madani, and J. Filipe,
editors, Computational Intelligence, volume 620 of Studies in Computational Intelligence, pages 113130. Springer Interna-
tional Publishing, 2016. ISBN 978-3-319-26391-5. doi: 10.1007/978-3-319-26393-9 8
 F. Bianchi, E. De Santis, A. Rizzi, and A. Sadeghian. Short-term electric load forecasting using echo state networks and
pca decomposition. Access, IEEE, 3:19311943, 2015. ISSN 2169-3536. doi: 10.1109/ACCESS.2015.2485943.
© Enrico De Santis - 2017