Shuang Gao is currently a postdoctoral researcher at GERAD and at the Centre for Intelligent Machine at McGill University. He received his PhD degree in Electrical Engineering from McGill University in February 2019, under the supervision of Professor Peter E. Caines, FRSC. His PhD work focused on the study of analysis and control of very large-scale networks (VLSNs) of multi-agent dynamical systems. A major part of the work pioneered exact and approximate (centralized and collaborative) control methodologies for linear dynamical systems on VLSNs based on graphon theory and the theory of infinite dimensional systems, a topic now called Graphon Control theory. Another topic of the work studies centrality measures for networks of multi-agent dynamical systems following consensus protocols. Dr. Gao’s postdoctoral work is on theoretical extensions, low-complexity solution methods and applications of Graphon Control and Graphon Mean Field Games theory, where the application work is currently focused on epidemic spread networks and biological neural networks. His other research interests include distributed and decentralized control, network modelling, and learning on large-scale networks.