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Dr Liang Qu

Post Doctoral Research Fellow

Staff Member Details
Email: l.qu@ecu.edu.au
ORCID iD: https://orcid.org/0000-0002-2755-7592

Liang is a Postdoctoral Research Fellow of Business Systems and Operations for the School of Business and Law.

Key research areas

  • Recommender systems
  • Federated learning
  • Privacy-preserving machine learning

Biography

Dr. Liang Qu obtained his PhD in Data Science from the University of Queensland in 2024. His research interests include recommender systems, federated learning, and graph neural networks, with a particular focus on privacy-preserving machine learning. He has published several papers in related conferences and journals and has also served as a reviewer. His work explores how privacy-preserving machine learning techniques can be effectively applied while safeguarding user and institutional privacy

Awards & Recognition

2024 – Best Student Paper Runner-up, The 20th International Conference Advanced Data Mining and Applications 2024.

Qualifications

  • Doctor of philosophy, The University of Queensland, 2024.

Research Outputs

2025

Journal Articles

  • Yuan, W., Yang, C., Qu, L., Ye, G., Nguyen, Q., Yin, H. (2025). Robust federated contrastive recommender system against targeted model poisoning attack. Science China Information Sciences, 68(4), article number 140103. https://doi.org/10.1007/s11432-024-4272-y.
  • Yuan, W., Yang, C., Qu, L., Hung, NQ., Ye, G., Yin, H. (2025). PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System. ACM Transactions on Information Systems, 43(2), article number 52. https://doi.org/10.1145/3708344.

Conference Publications

  • Yang, C., Yuan, W., Qu, L., Nguyen, T. (2025). PDC-FRS: Privacy-Preserving Data Contribution for Federated Recommender System. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (65-79). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-0850-8_5.

2024

Journal Articles

  • Zheng, R., Qu, L., Chen, T., Zheng, K., Shi, Y., Yin, H. (2024). Personalized Elastic Embedding Learning for On-Device Recommendation. IEEE Transactions on Knowledge and Data Engineering, 36(7), 3363-3375. https://doi.org/10.1109/TKDE.2024.3361562.

Conference Publications

  • Qu, Y., Qu, L., Chen, T., Zhao, X., Nguyen, Q., Yin, H. (2024). Scalable Dynamic Embedding Size Search for Streaming Recommendation. International Conference on Information and Knowledge Management, Proceedings (1941-1950). Association for Computing Machinery. https://doi.org/10.1145/3627673.3679638.
  • Zheng, R., Qu, L., Chen, T., Zheng, K., Shi, Y., Yin, H. (2024). Poisoning Decentralized Collaborative Recommender System and Its Countermeasures. SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (1712-1721). Association for Computing Machinery, Inc. https://doi.org/10.1145/3626772.3657814.
  • Qu, L., Wang, C., Shi, Y. (2024). Brain Storm Optimization based Swarm Learning for Diabetic Retinopathy Image Classification. 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings (1-Jul). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC60901.2024.10612118.
  • Yin, H., Chen, T., Qu, L., Cui, B. (2024). On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm. WWW 2024 Companion - Companion Proceedings of the ACM Web Conference (1280-1283). Association for Computing Machinery, Inc. https://doi.org/10.1145/3589335.3641250.
  • Yuan, W., Qu, L., Cui, L., Tong, Y., Zhou, X., Yin, H. (2024). HeteFedRec: Federated Recommender Systems with Model Heterogeneity. Proceedings - International Conference on Data Engineering (1324-1337). IEEE Computer Society. https://doi.org/10.1109/ICDE60146.2024.00109.
  • Yuan, W., Yang, C., Qu, L., Nguyen, Q., Li, J., Yin, H. (2024). Hide Your Model: A Parameter Transmission-free Federated Recommender System. Proceedings - International Conference on Data Engineering (611-624). IEEE Computer Society. https://doi.org/10.1109/ICDE60146.2024.00053.
  • Zheng, R., Qu, L., Chen, T., Cui, L., Shi, Y., Yin, H. (2024). Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation. WWW 2024 - Proceedings of the ACM Web Conference (3930-3939). Association for Computing Machinery, Inc. https://doi.org/10.1145/3589334.3645696.
  • Qu, L., Yuan, W., Zheng, R., Cui, L., Shi, Y., Yin, H. (2024). Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation. WWW 2024 - Proceedings of the ACM Web Conference (3910-3918). Association for Computing Machinery, Inc. https://doi.org/10.1145/3589334.3645690.

2023

Journal Articles

  • Zheng, R., Qu, L., Cui, B., Shi, Y., Yin, H. (2023). AutoML for Deep Recommender Systems: A Survey. ACM Transactions on Information Systems, 41(4), article number 101. https://doi.org/10.1145/3579355.

Conference Publications

  • Qu, L., Tang, N., Zheng, R., Nguyen, Q., Huang, Z., Shi, Y., Yin, H. (2023). Semi-decentralized Federated Ego Graph Learning for Recommendation. ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 (339-348). Association for Computing Machinery, Inc. https://doi.org/10.1145/3543507.3583337.

2022

Conference Publications

  • Qu, L., Ye, Y., Tang, N., Zhang, L., Shi, Y., Yin, H. (2022). Single-shot Embedding Dimension Search in Recommender System. SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (513-522). Association for Computing Machinery, Inc. https://doi.org/10.1145/3477495.3532060.

2021

Conference Publications

  • Qu, L., Zhu, H., Zheng, R., Shi, Y., Yin, H. (2021). ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (1390-1398). Association for Computing Machinery. https://doi.org/10.1145/3447548.3467334.
  • Qu, L., Zheng, R., Shi, Y. (2021). BSO-CMA-ES: Brain Storm Optimization Based Covariance Matrix Adaptation Evolution Strategy for Multimodal Optimization. Communications in Computer and Information Science (167-174). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7502-7_19.

2020

Conference Publications

  • Yang, J., Qu, L., Shen, Y., Shi, Y., Cheng, S., Zhao, J., Shen, X. (2020). Swarm Intelligence in Data Science: Applications, Opportunities and Challenges. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (3-14). Springer. https://doi.org/10.1007/978-3-030-53956-6_1.
  • Qu, L., Zhu, H., Shi, Y. (2020). BSOGCN: Brain Storm Optimization Graph Convolutional Networks Based Heterogeneous Information Networks Embedding. 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings (article number 9185532). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185532.
  • Qu, L., Zhu, H., Duan, Q., Shi, Y. (2020). Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (3026-3032). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380073.
  • Qu, L., Duan, Q., Yang, J., Cheng, S., Zheng, R., Shi, Y. (2020). BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (243-250). Springer. https://doi.org/10.1007/978-3-030-53956-6_22.

2019

Conference Publications

  • Qu, L., Shi, Y. (2019). Gradient-free Algorithms for Graph Embedding. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (2746-2752). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790139.
  • Duan, Q., Qu, L., Shao, C., Shi, Y. (2019). Hierarchical Decomposition based Cooperative Coevolution for Large-Scale Black-Box Optimization. 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (2690-2697). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI44817.2019.9003169.
  • Duan, Q., Shao, C., Qu, L., Shi, Y., Niu, B. (2019). When Cooperative Co-Evolution Meets Coordinate Descent: Theoretically Deeper Understandings and Practically Better Implementations. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (721-730). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790148.
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