About Me

I am a research fellow in the School of Information and Communication Technology at Griffith University. Previously, I was a research fellow in the School of Computer Science and Engineering at the University of New South Wales (UNSW). I earned my Ph.D. from Australian AI Institute, University of Technology Sydney, supervised by A/Prof. Guodong Long, A/Prof. Jing Jiang, and Prof. Chengqi Zhang. I obtained my B.S. and M.S. degrees from Beijing University of Posts and Telecommunications in 2017 and 2020, respectively, supervised by Prof. Kan Zheng and A/Prof. Lei Lei. I have also worked at Amazon, Sony AI, and NinjaTech AI as an applied scientist.

My research focuses on federated learning, robust and trustworthy machine learning, multimodal machine learning, and agentic AI. I am particularly interested in developing communication-efficient federated learning algorithms to solve heterogeneous problems in real-world applications. My work has been published in leading journals and conferences, including NeurIPS, ACL, AAAI, IJCAI, EMNLP, ICML, and KDD.

I am always open to research collaborations across diverse topics. Please feel free to contact me if you believe our research interests align.

News

• 05/2026: One paper on federated anomaly detection has been accepted by IJCAI 2026.
• 04/2026: One paper on multi-agent system safeguarding has been accepted by ACL 2026 (Main Conference).
• 01/2026: I am excited to share that our paper federated prototype learning has received over 1000 citations! 🎉
• 09/2025: My first-authored paper on multimodal understanding has been accepted to NeurIPS 2025.
• 07/2025: Our team achieved 3rd Place (US$1500) - KDD Cup 2025, Meta Comprehensive RAG Multi-modal, Multi-turn (CRAG-MM) Challenge.
• 10/2023: I am honoured to receive the NeurIPS 2023 Scholar Award.
• 09/2023: One paper on handling test-time shifts in federated learning has been accepted to NeurIPS 2023.
• 09/2023: Invited as Reviewer of ICLR 2024.
• 07/2023: Invited as Program Committee member of AAAI 2024.
• 07/2023: A tutorial on Recent Advancement on Federated Learning Combating Non-IID Data presented at IJCNN-2023.
• 06/2023: One paper accepted to the FL4Data-Mining workshop at KDD 2023.
• 04/2023: Invited as Reviewer of NeurIPS 2023.
• 11/2022: One paper on federated graph learning has been accepted to AAAI 2023 (Oral).
• 10/2022: I am honoured to receive the NeurIPS 2022 Scholar Award.
• 09/2022: One paper on federated learning has been accepted to NeurIPS 2022 (Spotlight).
• 07/2022: One paper on federated learning has been accepted to ICML Workshop 2022.
• 12/2021: One paper on federated prototype learning has been accepted to AAAI 2022.

Education

• Ph.D. (2024) in Computer Science, University of Technology Sydney
  Advisor: A/Prof. Guodong Long

• M.S. (2020) in Information and Communication Engineering, Beijing University of Posts and Telecommunications
  Advisor: Prof. Kan Zheng

• B.S. (2017) in an elite class, equivalent to an honoured degree, Beijing University of Posts and Telecommunications

Industrial Experiences

• Amazon, Applied Scientist Intern, Working with IML team led by Prof. Anton van den Hengel, 2023.
• Sony AI, Research Intern, Working with PPML team led by Dr. Lingjuan Lyu, 2023.

Honors & Awards

• 10/2023: NeurIPS 2023 Scholar Award.
• 01/2023: AAAI 2023 Travel Grant.
• 12/2022: Vice-Chancellor’s Conference Fund 2022 Round 3.
• 12/2022: AAII-UTS Best Paper Award.
• 10/2022: NeurIPS 2022 Scholar Award.
• 08/2021: Data61 Top-Up Scholarship.

Contact

• Email: yue.tan[at]griffith[dot]edu[dot]au

• Office: 170 Kessels Rd, Nathan QLD 4111