Zerun Niu is a Master of Philosophy candidate in Computer Science at the University of Sydney and a member of the DUAL Research Group. His research focuses on efficient and reliable distributed AI systems, including federated learning, edge intelligence, crowdsourced label aggregation, and graph-based knowledge representation for retrieval-augmented generation.

He is also a Casual Academic and Research Assistant at the University of Sydney, with experience in teaching, model optimization, federated learning systems, and cross-domain AI applications. His research has been published in IEEE Journal on Selected Areas in Communications, and he has served as a reviewer for IEEE Transactions on Machine Learning in Communications and Networking.

His current work aims to improve the robustness, efficiency, and probabilistic reliability of machine learning systems under communication, computation, and data-quality constraints.