Erfan Nozari received his B.Sc. degree in Electrical Engineering-Control in 2013 from Isfahan University of Technology, Iran, received his Ph.D. in Mechanical Engineering and Cognitive Science in 2019 from University of California San Diego, and was subsequently a postdoctoral researcher in the University of Pennsylvania Department of Electrical and Systems Engineering. He is currently an assistant professor at the University of Riverside, California Department of Mechanical Engineering.

He has been the (co)recipient of the 2019 IEEE Transactions on Control of Network Systems Outstanding Paper Award, the Best Student Paper Award from the 57th IEEE Conference on Decision and Control, the Best Student Paper Award from the 2018 American Control Conference, and the Mechanical and Aerospace Engineering Distinguished Fellowship Award from the University of California San Diego. His research interests include systems and control theory and machine learning and their applications in computational and theoretical neuroscience.

Fabio Pasqualetti is an Associate Professor in the Department of Mechanical Engineering, University of California at Riverside. He completed a Doctor of Philosophy degree in Mechanical Engineering at the University of California, Santa Barbara, in 2012, a Laurea Magistrale degree (M.Sc. equivalent) in Automation Engineering at the University of Pisa, Italy, in 2007, and a Laurea degree (B.Sc. equivalent) in Computer Engineering at the University of Pisa, Italy, in 2004. His main research interests include the analysis and control of complex networks, security of cyber-physical systems, distributed control, and network neuroscience. He has received several awards, including a Young Investigator Award from AFOSR in 2019, a Young Investigator Program award from ARO in 2017, and the 2016 TCNS Outstanding Paper Award from IEEE CSS.

Sérgio Pequito is an assistant professor in the Delft Center for Systems & Control that is part of the Mechanical, Maritime and Materials Engineering faculty at Delft University of Technology. Pequito's research consists of understanding the global qualitative behavior of large-scale systems from their structural or parametric descriptions and provides a rigorous framework for the design, analysis, optimization, and control of large scale systems. Currently, his interests span to neuroscience and biomedicine, where dynamical systems and control theoretic tools can be leveraged to develop new analysis tools for brain dynamics towards effective personalized medicine and improve brain-computer and brain-machine-brain interfaces.

Pequito was awarded the best student paper finalist in the 48th IEEE Conference on Decision and Control (2009) and the 2016 O. Hugo Schuck Award in the Theory Category by the American Automatic Control Council.