Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate



f.boem Website




1108Electrical EngineeringSouth Kensington Campus





Dr. Francesca Boem received the MSc degree (cum laude) in Management Engineering in 2009 and the PhD degree in Information Engineering in 2013, both from the University of Trieste, Italy.
She was Post-Doc at the Department of Engineering and Architecture at the University of Trieste from 2013 to 2014 with the Machine Learning Group.
Since October 2014, she is Research Associate at the Department of Electrical and Electronic Engineering, Imperial College London, with the Control and Power Research Group.
In 2015 - 2016 she has worked on the Phase I of the flagship EU H2020-WIDESPREAD-TEAMING project for the development of the KIOS Research and Innovation Centre of Excellence, a partnership between University of Cyprus and Imperial College London, which has been successful for Phase II funding. Since 2017 she is also with the KIOS Research and Innovation Center of Excellence.

Research Interests. Her current research interests include scalable methods for monitoring and fault-tolerant control for large-scale networked systems, with applications to power networks and critical infrastructures.



Boem F, Ferrari RMG, Keliris C, et al., 2016, A distributed networked approach for fault detection of large-scale systems, Ieee Transactions on Automatic Control, Vol:62, ISSN:0018-9286, Pages:18-33


Boem F, Gallo AJ, Ferrari-Trecate G, et al., A Distributed Attack Detection Method for Multi-Agent Systems Governed by Consensus-Based Control, 56th IEEE Conference on Decision and Control, IEEE

Boem F, Reci R, Cenedese A, et al., 2017, Distributed Clustering-based Sensor Fault Diagnosis for HVAC Systems, Pages:4197-4202

Li P, Boem F, Pin G, et al., 2017, Distributed Fault Detection and Isolation for Interconnected Systems: a Non-Asymptotic Kernel-Based Approach, Pages:1013-1018

Zhou Y, Boem F, Parisini T, 2017, Partition-based Pareto-optimal state prediction method for interconnected systems using sensor networks, Pages:1886-1891, ISSN:0743-1619

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