Lia Song

Dr Lia Song

Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology


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
    2024 Zhang, Q., Lai, N., He, M., Yang, Y., Huang, Q., Quan, Y., . . . Wang, R. (2024). Tunable broadband luminescence of Bi-ion-doped glasses via Gd<sub>2</sub>O<sub>3</sub> co-doping. JOURNAL OF THE AMERICAN CERAMIC SOCIETY, 107(6), 8 pages.
    DOI
    2024 Abbasnejad, B., Nasirian, A., Duan, S., Diro, A., Prasad Nepal, M., & Song, Y. (2024). Measuring BIM Implementation: A Mathematical Modeling and Artificial Neural Network Approach. Journal of Construction Engineering and Management, 150(5).
    DOI
    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 Scopus4 WoS1
    2023 Yu, H., Li, J., Lu, J., Song, Y., Xie, S., & Zhang, G. (2023). Type-LDD: A Type-Driven Lite Concept Drift Detector for Data Streams. IEEE Transactions on Knowledge and Data Engineering, 1-14.
    DOI Scopus1
    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 Scopus6 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 Scopus13 WoS7
    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 Scopus19 WoS11
    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 Scopus10 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 Scopus9 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 Scopus18 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 Scopus8 WoS7 Europe PMC3
    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 Scopus8 WoS9 Europe PMC1
    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 Scopus23 WoS21 Europe PMC5
    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 Scopus32 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 Scopus50 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 Scopus102 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 Scopus59 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 Scopus73 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 Scopus24 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 Scopus204 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 Scopus45 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 Scopus72 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 Scopus47 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). ELECTR NETWORK: IEEE.
    DOI Scopus3
    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). ELECTR NETWORK: IEEE.
    DOI Scopus9 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. IEEE.
    DOI Scopus5
    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 Scopus3
    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 Scopus67 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 Scopus10 WoS12
  • 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

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