Massimo Franceschetti

Massimo Franceschetti is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), with faculty affiliation at Calit2. His research spans mathematical engineering, focusing on control, communication, computation, and sensing, particularly in complex networks and systems. He integrates tools from statistical physics, wave propagation, and information theory to analyze and design networked systems.

• Born in Naples, Italy, he studied at the University of Naples Federico II and the University of Edinburgh (European exchange program), graduating in 1997. He earned his M.Sc. (1999) and PhD (2003) from Caltech, where he received the Walker von Brimer Award and the C.H. Wiltz Prize for outstanding research and thesis.
• After postdoctoral work at UC Berkeley (2003-2004), he joined UCSD as faculty and held visiting positions at Vrije Universiteit Amsterdam, EPFL (Switzerland), and the University of Trento (Italy). He became an IEEE Fellow in 2018 and was nominated a Guggenheim Fellow in 2019.

His research includes networked control systems, stochastic geometry, electromagnetic information theory, and social dynamical systems. Recent work explores non-invasive emotional contagion in social networks, quantum limits on information entropy, and the physics of wave propagation. His publications bridge information theory, machine learning, and network science, often applying percolation theory and random walks to explain scaling laws and wireless signal behavior.

Scientific accolades include the S.A. Schelkunoff Transactions Prize, IEEE Communications Society Best Tutorial Paper Award, and the IEEE Ruberti Young Researcher Prize. He co-authored two books: Random Networks for Communication (2007) and Wave Theory of Information (2018). His students have pursued careers in academia (e.g., IIT-Bombay, Notre Dame) and industry (e.g., Google, IBM, Tesla).

He teaches courses on network science, information theory, and control systems, emphasizing data-driven analysis and the physical foundations of communication. His group’s work impacts cyber-physical systems, quantum network coding, and epidemic modeling on networks.