Giorgio Picci is an Italian electrical engineer and control theorist. He has made widely recognized contributions to the field of Systems and Control, and in particular, to the areas of Stochastic Realization and System Identification. He was also a pioneer in Computer Vision.[1][2] He has published a monograph (in English)[3] and over 150 papers in these areas.
He is currently a Professor Emeritus with the Department of Information Engineering, University of Padova, Italy.[4] He is also the Italian director of the Confucius Institute at the University of Padova,[5] where he actively promotes cultural and scientific exchanges between China and Italy,[6] in collaborations with Guangzhou University in China.
Lindquist, A., & Picci, G. (2015). Series in Contemporary Mathematics: vol. 1, Linear Stochastic Systems: A Geometric Approach to Modeling, Estimation and Identification. Springer-Verlag, Berlin Heidelberg.[3] This book was translated into Chinese and published in two volumes by the Shanghai Scientific and Technical Publishers in 2018.[12]
Picci, G. (2024). An Introduction to Statistical Data Science: Theory and Models. Springer, Cham.[13]
Picci, G. (2007). Filtraggio Statistico (Wiener, Levinson, Kalman) e Applicazioni (in Italian). Progetto Libreria, Padova.[14]
Edited books:
Picci, G. & Valcher, M. E. eds. (2007). A Tribute to Antonio Lepschy. Libreria Progetto, Padova.[15]
Picci, G. & Gilliam, D. S. eds. (1999). Progress in Systems and Control Theory: vol. 25, Dynamical Systems, Control, Coding, Computer Vision: New trends, Interfaces, and Interplay. Birkhäuser Verlag, Basel.[16]
Beghi, A., Finesso, L., & Picci, G. eds. (1998). Mathematical Theory of Networks and Systems: Proceedings of the MTNS-98 Symposium Held in Padova, Italy, July, 1998, Il Poligrafo, Padova.[17]
Bittanti, S. & Picci, G. eds. (1996). NATO ASI Subseries F: vol. 153, Identification, Adaptation, Learning: The Science of Learning Models from Data. Springer-Verlag, Berlin Heidelberg.[18]
Cao, W., Lindquist, A., & Picci, G. (2023). Modeling of low rank time series. IEEE Transactions on Automatic Control, 68(12), 7270-7285.[19]
Cao, W., Picci, G., & Lindquist, A. (2023). Identification of low rank vector processes. Automatica, 151, article number: 110938.[20]
Picci, G. (2023). Periodic vector processes with an internal Reciprocal Dynamics. Systems & Control Letters, 174, article number: 105503.[21]
Picci, G., Falconi, L., Ferrante, A., & Zorzi, M. (2023). Hidden factor estimation in Dynamic Generalized Factor Analysis models. Automatica, 149, article number: 110834.[22]
Picci, G. & Zhu, B. (2022). Empirical Bayes identification of stationary processes and approximation of Toeplitz spectra. Automatica, 142, article number: 110362.[23]
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