The research interests of Laurent Massoulié include unsupervised learning, distributed machine learning, modeling and algorithmic design for distributed systems and networks. His work has led to significant advancements in the field, particularly in the areas of congestion control, P2P networks and epidemic processes, community detection, and distributed optimization for federated learning. This research has provided critical insights into fundamental performance trade-offs and resolution of open problems, and allowed for the design of new algorithms for distributed control of systems.
His work has been influential and transformative, not just within the SIGMETRICS community, but also in other communities such as applied mathematics, theoretical computer science, and information theory.