Lia Song

Dr Lia Song

Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

My research interests are in streaming data mining and trustworthy AI. My current working topics include:
· Modeling with distributional changes (detection and adaptation)
· Real-time prediction for streaming data
· Privacy protection in federated learning
· AI explainability

I am interested in trustworthy AI, especially the robustness, security, and explainability of machine learning models. My current research includes the following topics:

  • Modeling with distributional changes (detection and adaptation)
  • Real-time prediction for streaming data
  • Privacy protection in federated learning
  • AI explainability

  • Journals
    Year Citation
    2025 Gong, M., Song, Y., Koh, Y. S., Xiang, W., & Wang, D. (2025). Preface. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15442 LNAI, v-vi.
    2025 Gong, M., Song, Y., Koh, Y. S., Xiang, W., & Wang, D. (2025). Preface. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15443 LNAI, v-vi.
    2025 Chu, R., Chik, L., Song, Y., Chan, J., & Li, X. (2025). An effective approach for early fuel leakage detection with enhanced explainability. Intelligent Systems with Applications, 26, 200504.
    DOI
    2024 Ma, W., Song, Y., Xue, M., Wen, S., & Xiang, Y. (2024). The 'Code' of Ethics: A Holistic Audit of AI Code Generators. IEEE Transactions on Dependable and Secure Computing, 21(5), 4997-5013.
    DOI Scopus3
    2023 Liu, A., Lu, J., Song, Y., Xuan, J., & Zhang, G. (2023). Concept Drift Detection Delay Index. IEEE Transactions on Knowledge and Data Engineering, 35(5), 4585-4597.
    DOI Scopus14 WoS2
    2023 Song, Y., Lu, J., Lu, H., & Zhang, G. (2023). Learning Data Streams with Changing Distributions and Temporal Dependency. IEEE Transactions on Neural Networks and Learning Systems, 34(8), 3952-3965.
    DOI Scopus19 WoS7 Europe PMC1
    2023 Zhou, M., Lu, J., Song, Y., & Zhang, G. (2023). Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12828-12841.
    DOI Scopus13 WoS1
    2022 Yu, E., Song, Y., Zhang, G., & Lu, J. (2022). Learn-to-adapt: Concept drift adaptation for hybrid multiple streams. Neurocomputing, 496, 121-130.
    DOI Scopus18 WoS6
    2022 Dong, F., Lu, J., Song, Y., Liu, F., & Zhang, G. (2022). A Drift Region-Based Data Sample Filtering Method. IEEE Transactions on Cybernetics, 52(9), 9377-9390.
    DOI Scopus11 WoS7
    2022 Song, Y., Lu, J., Liu, A., Lu, H., & Zhang, G. (2022). A Segment-Based Drift Adaptation Method for Data Streams. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4876-4889.
    DOI Scopus22 WoS13
    2022 Liu, B., Liao, J., Song, Y., Chen, C., Ding, L., Lu, J., . . . Wang, F. (2022). Multiplexed structured illumination super-resolution imaging with lifetime-engineered upconversion nanoparticles. Nanoscale Advances, 4(1), 30-38.
    DOI Scopus15 WoS7 Europe PMC5
    2022 Wang, K., Lu, J., Liu, A., Song, Y., Xiong, L., & Zhang, G. (2022). Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing, 491, 288-304.
    DOI Scopus30 WoS11
    2021 Liao, J., Zhou, J., Song, Y., Liu, B., Lu, J., & Jin, D. (2021). Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning. Journal of Physical Chemistry Letters, 12(41), 10242-10248.
    DOI Scopus13 WoS9 Europe PMC2
    2021 Liao, J., Zhou, J., Song, Y., Liu, B., Chen, Y., Wang, F., . . . Jin, D. (2021). Preselectable Optical Fingerprints of Heterogeneous Upconversion Nanoparticles. Nano Letters, 21(18), 7659-7668.
    DOI Scopus29 WoS21 Europe PMC6
    2020 Song, Y., Lu, J., Lu, H., & Zhang, G. (2020). Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams. IEEE Transactions on Fuzzy Systems, 28(3), 544-557.
    DOI Scopus42 WoS103
    2020 Lu, J., Liu, A., Song, Y., & Zhang, G. (2020). Data-driven decision support under concept drift in streamed big data. Complex and Intelligent Systems, 6(1), 157-163.
    DOI Scopus63 WoS41
    2017 Jiang, P., Liu, F., & Song, Y. (2017). A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting. Energy, 119, 694-709.
    DOI Scopus112 WoS88
    2017 Song, Y., Wang, Y., Liu, F., & Zhang, Y. (2017). Development of a hybrid model to predict construction and demolition waste: China as a case study. Waste Management, 59, 350-361.
    DOI Scopus74 WoS40 Europe PMC13
    2016 Jiang, P., Liu, F., & Song, Y. (2016). A hybrid multi-step model for forecasting day-ahead electricity price based on optimization, fuzzy logic and model selection. Energies, 9(8), 27 pages.
    DOI Scopus10 WoS7
    2016 Wang, J., Liu, F., Song, Y., & Zhao, J. (2016). A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Applied Soft Computing Journal, 48, 281-297.
    DOI Scopus81 WoS60
    2016 Jiang, P., Liu, F., Wang, J., & Song, Y. (2016). Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series. Applied Mathematical Modelling, 40(23-24), 9692-9718.
    DOI Scopus26 WoS18
    2016 Zhang, Z., Song, Y., Liu, F., & Liu, J. (2016). Daily average wind power interval forecasts based on an optimal adaptive-network-based fuzzy inference system and singular spectrum analysis. Sustainability (Switzerland), 8(2), 30 pages.
    DOI Scopus9 WoS7
    2016 Wang, J., Song, Y., Liu, F., & Hou, R. (2016). Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models. Renewable and Sustainable Energy Reviews, 60, 960-981.
    DOI Scopus227 WoS175
    2015 Qin, S., Liu, F., Wang, C., Song, Y., & Qu, J. (2015). Spatial-temporal analysis and projection of extreme particulate matter (PM<inf>10</inf> and PM<inf>2.5</inf>) levels using association rules: A case study of the Jing-Jin-Ji region, China. Atmospheric Environment, 120, 339-350.
    DOI Scopus47 WoS38
    2015 Song, Y., Qin, S., Qu, J., & Liu, F. (2015). The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region. Atmospheric Environment, 118, 58-69.
    DOI Scopus83 WoS67
    2015 Qin, S., Liu, F., Wang, J., & Song, Y. (2015). Interval forecasts of a novelty hybrid model for wind speeds. Energy Reports, 1, 8-16.
    DOI Scopus48 WoS45

  • Conference Papers
    Year Citation
    2021 Zhou, M., Song, Y., Zhang, G., Zhang, B., & Lu, J. (2021). An Efficient Bayesian Neural Network for Multiple Data Streams. In Proceedings of the International Joint Conference on Neural Networks Vol. 2021-July (pp. 8 pages). Shenzhen, China: IEEE.
    DOI Scopus4
    2020 Song, Y., Zhang, G., Lu, H., & Lu, J. (2020). A fuzzy drift correlation matrix for multiple data stream regression. In IEEE International Conference on Fuzzy Systems Vol. 2020-July (pp. 6 pages). Glasgow, UK: IEEE.
    DOI Scopus12 WoS11
    2019 Song, Y., Zhang, G., Lu, H., & Lu, J. (2019). A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression. In IEEE International Conference on Fuzzy Systems Vol. 2019-June (pp. 6 pages). LA, New Orleans: IEEE.
    DOI Scopus8
    2018 Song, Y., Zhang, G., Lu, H., & Lu, J. (2018). A self-adaptive fuzzy network for prediction in non-stationary environments. In IEEE International Conference on Fuzzy Systems Vol. 2018-July (pp. 8 pages). BRAZIL, Rio de Janeiro: IEEE.
    DOI Scopus5
    2017 Liu, A., Song, Y., Zhang, G., & Lu, J. (2017). Regional concept drift detection and density synchronized drift adaptation. In C. Sierra (Ed.), IJCAI International Joint Conference on Artificial Intelligence Vol. 0 (pp. 2280-2286). AUSTRALIA, Melbourne: IJCAI-INT JOINT CONF ARTIF INTELL.
    DOI Scopus88 WoS27
    2017 Song, Y., Zhang, G., Lu, J., & Lu, H. (2017). A fuzzy kernel c-means clustering model for handling concept drift in regression. In IEEE International Conference on Fuzzy Systems (pp. 6 pages). ITALY, Naples: IEEE.
    DOI Scopus13 WoS12

  • Conference Items
    Year Citation
    2024 Zhang, S., Song, Y., Yang, J., Li, Y., Han, B., & Tan, M. (2024). DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE OPTIMIZATION FOR MAXIMUM MEAN DISCREPANCY. Poster session presented at the meeting of 12th International Conference on Learning Representations, ICLR 2024.
    Scopus1
    2024 Wu, D., Bai, J., Song, Y., Chen, J., Zhou, W., Xiang, Y., & Sajjanhar, A. (2024). FEDINVERSE: EVALUATING PRIVACY LEAKAGE IN FEDERATED LEARNING. Poster session presented at the meeting of 12th International Conference on Learning Representations, ICLR 2024.
    Scopus3
  • UoA Start-up Grant - Chief Investigator (2023-2025)
  • BITS Pilani-RMIT PhD Program - Chief Investigator (2023-2027) Adversarial deep learning algorithms applied to outlier detection in dynamic networks
  • Data61 Net Gen Graduate Program - Associate Investigator (2023-2027) Developing Digital Capabilities to Support the Aged Care Sector
  • UTS Cross Faculty Scheme - Chief Investigator (2022-2023) AI-empowered Privacy and Ethics Risk Assessment Tool

2024

  • COMPSCI 2008, Topics in Computer Science
  • COMPSCI 4405/7405 Research Methods in Software Engineering and Computer Science

2023

  • ECON1555, Business Data Analytics (PG), RMIT University
  • AI in Web3, RMIT University

2022

  • ECON1555, Business Data Analytics (PG), RMIT University
  • Introduction to Software Development (UG), University of Technology Sydney

 

  • Position: Lecturer
  • Phone: 83133918
  • Email: lia.song@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor Level Four
  • Room: 4.39
  • Org Unit: School of Computer and Mathematical Sciences
Connect With Me
External Profiles