Keynote Speaker I


Prof.Sandro Zampieri 

IEEE Fellow
University of Padova, Italy

Sandro Zampieri received the Laurea degree in Electrical Engineering and the Ph.D. degree in System Engineering from the University of Padova, Italy, in 1988 and 1993, respectively. Since 2002 he is Full Professor in Automatic Control at the Department of Information Engineering of the University of Padova. He has been the head of the Department of Information Engineering from 2014 until 2018.

Zampieri has published more than 150 journal and conference papers. He was general chair of the 1st IFAC Workshop on Estimation and Control of Networked Systems 2009, program chair of the 3rd IFAC Workshop on Estimation and Control of Networked Systems 2012 and publication chair of the IFAC World Congress 2011. He served as an Associate Editor of the Siam Journal on Control and Optimization on 2002-2004 and of the IEEE Transactions of Automatic Control on 2012-2014. He was the chair of the IFAC technical committee "Networked systems" on 2005-2008. He was one of the recipients of the 2016 IEEE Transactions on Control of Network Systems Best Paper Award. He is IEEE Fellow since 2022. His research interests include networked control, control of complex systems and distributed control and estimation with applications to the smart grids.


Classical approaches to system identification are based on parametric estimation paradigms from mathematical statistics. In this setting, a key point is the selection of the most adequate model structure which is typically performed via complexity measures such as the Akaike's criterion. Starting from the linear scenario, then moving to the nonlinear one, this talk will describe how the model selection problem can be successfully faced by a different approach based on regularization theory. In particular, I will discuss the use of Bayesian kernel-based methods where the unknown system is seen as a Gaussian process whose covariance (kernel) includes information on system stability and/or fading memory. Here, tuning of model complexity gets a whole new dimension and richness in the choice of (continuous) regularization parameters compared to the choice of (discrete) model orders.

Keynote Speaker II

Prof.Prof. Francesco Bullo

IEEE Fellow, IFAC Fellow, SIAM Fellow
College of Engineering, University of California, Santa Barbara, USA

Francesco Bullo is a Professor with the Mechanical Engineering Department and the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was previously associated with the University of Padova (Laurea degree in Electrical Engineering, 1994), the California Institute of Technology (Ph.D. degree in Control and Dynamical Systems, 1999), and the University of Illinois. He served on the editorial boards of IEEE, SIAM, and ESAIM journals and as IEEE CSS President. His research interests focus on network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of “Geometric Control of Mechanical Systems” (Springer, 2004), “Distributed Control of Robotic Networks” (Princeton, 2009), and “Lectures on Network Systems” (Kindle Direct Publishing, 2021, v1.5). He received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a Fellow of IEEE, IFAC, and SIAM.


To be added...

Keynote Speaker III


Prof. Tarek M. Sobh 

Fellow, African Academy of Sciences
Lawrence Technological University, USA

Professor Tarek M. Sobh received the B.Sc. in Engineering degree with honors in Computer Science and Automatic Control from the Faculty of Engineering, Alexandria University, Egypt in 1988, and M.S. and Ph.D. degrees in Computer and Information Science from the School of Engineering, University of Pennsylvania in 1989 and 1991, respectively. He is currently the President and Professor of Electrical and Computer Engineering at Lawrence Technological University (LTU), Michigan. He is Distinguished Professor and Dean of Engineering Emeritus at the University of Bridgeport (UB), Connecticut.

Previously, he served as Provost of Lawrence Technological University (LTU) (2020-2021). At the University of Bridgeport (UB), he was the University Executive Vice President, Research and Economic Development (2018-2020), Interim Provost (2020), Founding Dean of the College of Engineering, Business, and Education (2018-2020), and Distinguished Professor of Engineering and Computer Science (2010-2020), the Founding Director of the Interdisciplinary Robotics, Intelligent Sensing, and Control (RISC) laboratory, Founder of the High-Tech Business Incubator at UB (CTech IncUBator); and the Founding Director of the Bauer Hall Innovation Center. He was the Senior Vice President for Graduate Studies and Research (2014-2018), Vice President (2008-2014), Vice Provost (2006-2008), Dean of the School of Engineering (1999-2018), Interim Dean of the School of Business, Director of External Engineering Programs, Interim Chair of Computer Science and Computer Engineering, and Chair of the Department of Technology Management.  He also served at UB as a Professor of Computer, Electrical and Mechanical Engineering and Computer Science from 2000-2010, and an Associate Professor of Computer Science and Computer Engineering from 1995-1999. He was a Research Assistant Professor of Computer Science at the Department of Computer ScienceUniversity of Utah from 1992-1995, and a Research Fellow at the General Robotics and Active Sensory Perception (GRASP) Laboratory of the University of Pennsylvania from 1989-1991. He was the Founding Chair of the Discrete Event and Hybrid Systems Technical Committee of the IEEE Robotics and Automation Society from 1992-1999, and the Founding Chair of the Prototyping Technical Committee of the IEEE Robotics and Automation Society from 1999-2001.

His background is in the fields of robotics, computer science and engineering, control theory, automation, manufacturing, AI, computer vision and signal processing.


To be added...