Marios Polycarpou - Selected Publications#

According to Google Scholar, the nominee's publications have received a total of over 21,000 citations and have an h-index of 70 as of February 2022. This ranks him 1st in Cyprus in the discipline of Computer Science. Moreover, according to the Stanford Citation Ranking of the top 2% Scientists, the nominee is ranked 1st in the University of Cyprus and one of the top scientists in the world in the area of computer science and engineering.

Below are 10 major publications of the nominee. The list is a combination of works that are responsible for the nominee's international recognition, as well as recent high-impact publications that appeared in top journals.

[1] M. Polycarpou, “Stable Adaptive Neural Control Scheme for Nonlinear Systems,” IEEE Transactions on Automatic Control, vol. 41, no. 3, pp. 447‐451, March 1996.

(one of the first works to show rigorously the stability of neural networks in a feedback loop; 1,314 citations)

[2] E. Kosmatopoulos, M. Polycarpou, M. Christodoulou, and P. Ioannou, “High‐Order Neural Network Structures for Identification of Dynamical Systems,” IEEE Transactions on Neural Networks, vol. 6, no. 2, pp. 422‐431, March 1995.

(introduced the design of high-order neural network structures, which have since been used extensively in many applications; 913 citations)

[3] M. Polycarpou and A. Helmicki, “Automated Fault Detection and Accommodation: A Learning System Approach,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 25, no. 11, pp. 1447‐1458, November 1995.

(the first paper to introduce a learning methodology for fault diagnosis, which had significant impact in the field of intelligent monitoring for dynamical systems; 380 citations)

[4] J. Farrell and M. Polycarpou, Adaptive Approximation Based Control, Hoboken, NJ: J. Wiley, 2006.

(one of the top textbooks on neural control that is used in universities worldwide for teaching the graduate course in intelligent systems and control; 639 citations)

[5] J. Bissler, M. Polycarpou, N. Hemasilpin and E. Morales, “Hemofiltration System”, U.S. Patent 6,780,322, issued on August 24, 2004.

(patent that invented an intelligent hemofiltration system, which was used successfully at the Childrens’ Hospital at the University of Cincinnati, USA; 169 citations)

[6] M. P. Michaelides, V. Reppa, M. Christodoulou, C. Panayiotou and M. Polycarpou, “Contaminant Event Monitoring in Multi-zone Buildings Using the State-Space Method,” Building and Environment (Elsevier), vol. 71, pp. 140-152, January 2014.

(the paper introduced for the first time a state-space approach for contaminant event monitoring; it was awarded the 2014 Best Paper Award for the Building and Environment journal).

[7] V. Reppa, M. Polycarpou and C. Panayiotou, “Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems", IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 1, pp. 137-153, January 2014.

(first paper to design an adaptive approximation approach for multiple sensor fault detection and isolation. The paper was nominated for the Outstanding Paper Award)

[8] C. Heracleous, P. Kolios, C. Panayiotou, G. Ellinas, M. Polycarpou, “Hybrid Systems Modeling for Critical Infrastructures Interdependency Analysis”, Reliability Engineering and System Safety, vol. 165, pp. 89-101, September 2017.

(this was the first work to propose a hybrid modeling approach for capturing the interdependencies among different critical infrastructure systems)

[9] G. Milis, C. Panayiotou, M. Polycarpou, “SEMIoTICS: Semantically-enhanced IoT-enabled Intelligent Control Systems”, IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1257-1266, 2019.

(development of a novel approach for designing semantic control systems to enhance the capability for plug-and-play. This approach was awarded an ERC Proof-of-Concept grant and resulted in a start-up company)

[10] K. Malialis, C.G. Panayiotou and M. Polycarpou, “Online Learning With Adaptive Rebalancing in Nonstationary Environments”, IEEE Transactions on Neural Networks and Learning Systems, 2 vol. 32, no. 10, pp. 4445-4459, October 2021.

(this is the first work to develop an adaptive strategy for online learning in nonstationary environments with unbalanced data)