desantisEnrico 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

e-mail: enrico.desantis(at)uniroma1.it

enrico.desantis(at)ryerson.ca

 

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 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 ambit, he is interested in data mining and knowledge extraction to make smart decisions in changing environments.

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. Advanced Artificial Neural Netwoks have ben modeled for prediction tasks.

He is also interested in nature-inspired learning theories and how biological systems, such as brains, and silicon-based Information Systems, represent knowledge.

 

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Blog (mostly in italian)

Publications

[1] 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

418–423, June 2013. doi: 10.1109/IFSA-NAFIPS.2013.6608437.

[2] 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 1949–1956, July 2014. doi:

10.1109/IJCNN.2014.6889668.

[3] 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 95–103, 2014. ISBN 978-989-758-052-9. doi: 10.5220/0005124800950103.

[4] 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://

dx.doi.org/10.1016/j.neucom.2015.05.112.

[5] 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

1–8, July 2015. doi: 10.1109/IJCNN.2015.7280756.

[6] 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 113–130. Springer Interna-

tional Publishing, 2016. ISBN 978-3-319-26391-5. doi: 10.1007/978-3-319-26393-9 8

[7] F. Bianchi, E. De Santis, A. Rizzi, and A. Sadeghian. Short-term electric load forecasting using echo state networks and

pca decomposition. Access, IEEE, 3:1931–1943, 2015. ISSN 2169-3536. doi: 10.1109/ACCESS.2015.2485943.

[8] F. M. Bianchi, E. De Santis, P. Naraei, H. Montazeri, and A. Sadeghian. Position paper: a general framework for applying machine

 learning techniques in operating room. Arxiv preprint, 2015 (first two authors contributed equally).

[9] S. Leonori, E. De Santis, A. Rizzi and F.M.F. Mascioli. Multi objective optimization of a fuzzy logic controller for energy management

  in microgrids. Evolutionary Computation (CEC), 2016 IEEE Congress on, 319-326. DOI: 10.1109/CEC.2016.7743811

[10] S. Leonori, E. De Santis, A. Rizzi and F.M.F. Mascioli. Optimization of a microgrid energy management system

  based on a Fuzzy Logic Controller. Industrial Electronics Society, IECON 2016-42nd, 2016. DOI: 10.1109/IECON.2016.7793965

© Enrico De Santis  - 2017