This talk explains how agents over a graph can learn from dispersed information and solve inference tasks of varying degrees of complexity through localized processing. The presentation also shows how information or misinformation is diffused over graphs, how beliefs are formed, and how the graph topology helps resist or enable manipulation. Examples will be considered in the context of social learning, teamwork, distributed optimization, and adversarial behavior.
A. H. Sayed is Dean of Engineering at EPFL, Switzerland, where he also leads the Adaptive Systems Laboratory (https://asl.epfl.ch/). He is recognized as a Highly Cited Researcher, is a member of the US National Academy of Engineering, and served as President of the IEEE Signal Processing Society in 2018 and 2019. His research involves several areas including adaptation and learning theories, data and network sciences, statistical inference, information processing theories, and multi-agent systems. His work has been recognized with several major awards from IEEE and other professional societies.