MAE Seminar- Tractable Network Interventions for Large Scale Socio-Technical Systems

Abstract: Modern socio-technical systems involve a large number of nodes or agents interacting in heterogeneous ways. It is clear that interventions aimed at improving the performance or resilience of these systems should exploit information about the underlying network of interactions, yet most systems of interest are of very large dimension introducing several challenges for the design of network based interventions. In this talk, I will illustrate some of these challenges and possible ways to overcome them in the context of power and economics networks.

 First, motivated by applications in high voltage electric grids, I will discuss interventions aimed at ensuring robust synchronicity in networks of coupled phase-oscillators. Specifically, I will discuss how to optimally allocate network edge weights to minimize a measure of network vulnerability proposed by Tyloo et. al (quantifying how much a small perturbation to a phase-oscillator’s natural frequency impacts the system’s global synchronized frequencies). I will show that this problem can be reformulated as a tractable semidefinite programming problem and I will illustrate how the obtained result can support optimal placement of renewable generation.

 Second, I will consider strategic decision making in social and economic networks. In this case, the underlying network of interactions may be unknown, as collecting exact network data might be either too costly or impossible due to privacy concerns. Moreover, methods for designing optimal interventions that rely on the exact network data typically do not scale well with the population size. To obviate these issues, I will introduce the tool of “graphon games” as a way to formally describe strategic interactions in uncertain network settings and I will illustrate how this tool can be exploited to design interventions that are asymptotically optimal in terms of the population size and can be efficiently computed without requiring exact network data.

Dr. Francesca Parise joined the School of Electrical and Computer Engineering at Cornell University as an assistant professor in July 2020. Before then, she was a postdoctoral researcher at the Laboratory for Information and Decision Systems at MIT. She defended her PhD at the Automatic Control Laboratory, ETH Zurich, Switzerland in 2016 and she received the B.Sc. and M.Sc. degrees in Information and Automation Engineering in 2010 and 2012, from the University of Padova, Italy, where she simultaneously attended the Galilean School of Excellence. Francesca’s research focuses on identification, analysis and control of multi-agent systems, with application to transportation, energy, social and economic networks. 

Francesca was recognized as an EECS rising star in 2017 and is the recipient of the Guglielmo Marin Award from the “Istituto Veneto di Scienze, Lettere ed Arti”, the SNSF Early Postdoc Fellowship, the SNSF Advanced Postdoc Fellowship and the ETH Medal for her doctoral work.