Guodong Long

Also known as Long Guodong

Guodong Long is a professor of computer science at the University of Technology Sydney (UTS) whose research covers federated learning, a way of training models across decentralized user data, and graph neural networks. A UTS PhD holder, he rose from research associate in 2014 to professor in January 2026, and he chairs the Australian Computer Society's National AI Committee. In 2019, he co-authored A Comprehensive Survey on Graph Neural Networks and Graph WaveNet, and the survey received the 2024 IEEE CIS Transactions on Neural Networks and Learning Systems Outstanding Paper Award. His federated learning papers include Multi-Center Federated Learning in 2020, which he first-authored, and FedProto in 2021, and he leads UTS's Foundation Models and Federated Learning research group. He co-chaired the ACM Web Conference 2025 in Sydney and was awarded an ARC Future Fellowship in September 2026 for a project on federated agentic AI.

Now at: University of Technology Sydney

Timeline

2014–2021

Research associate to senior lecturer

A PhD holder from the University of Technology Sydney (UTS), he starts in the university's Faculty of Engineering and IT as a research associate in 2014. He becomes a lecturer (assistant professor) in 2015 and a senior lecturer in 2017.
2019

Graph neural network papers

In January 2019, he co-authors A Comprehensive Survey on Graph Neural Networks with Zonghan Wu, Shirui Pan, Fengwen Chen, Chengqi Zhang and Philip S. Yu; it proposes a taxonomy that divides graph neural networks into four categories. In May 2019, he co-authors Graph WaveNet, a graph neural network architecture for spatial-temporal graph modeling.
2020–2021

Federated learning papers

In 2020, he is first author of Multi-Center Federated Learning, which learns multiple global models from decentralized, non-IID user data and matches users to centers. In 2021, he co-authors FedProto, in which the server aggregates local class prototypes from clients and sends global prototypes back, and the chapter Federated Learning for Open Banking.
January 2021–January 2026

Associate professor at UTS

In January 2021, he becomes an associate professor in UTS's Faculty of Engineering and IT. During 2021, he also serves as program co-chair of the Australasian Joint Conference on Artificial Intelligence.
2024

A paper award and AI 2000

A Comprehensive Survey on Graph Neural Networks, which appears in IEEE Transactions on Neural Networks and Learning Systems in 2021 with him as a co-author, receives the 2024 IEEE CIS TNNLS Outstanding Paper Award. AMiner's annual AI 2000 list ranks him #38 worldwide among influential AI scholars in January 2024.
November 2024

Federated foundation models grant

In the ARC Discovery Projects round for 2025, announced in November 2024, he is lead investigator of Federated Foundation Models for Recommendations (DP250101576), a project to build a recommendation framework with privacy preservation and fine-grained personalisation. He leads UTS's Foundation Models and Federated Learning research group.
2025

Co-chairing the ACM Web Conference

The ACM Web Conference Steering Committee announces that a team led by him and Michael Blumenstein of UTS and Yi Chang of Jilin University will organise WebConf 2025 in Sydney. As first general co-chair, he heads a cross-national organising committee, and the conference welcomes over 1,400 delegates at ICC Sydney in 2025.
Since December 2025

Professor and ARC Future Fellow

He chairs the Australian Computer Society's National AI Committee from December 2025 and becomes a professor at UTS in January 2026. In September 2026, UTS announces his ARC Future Fellowship of $1,328,536 for the four-year project Building the Foundations of Federated Agentic AI, which runs from 2027 to 2031.