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
    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<inf>2</inf>O<inf>3</inf> co-doping. Journal of the American Ceramic Society, 107(6), 3837-3844.
    DOI
    2024 Ma, W., Song, Y., Xue, M., Wen, S., & Xiang, Y. (2024). The &amp;#x201C;Code&amp;#x201D; of Ethics: A Holistic Audit of AI Code Generators. IEEE Transactions on Dependable and Secure Computing, 21(5), 4997-5013.
    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), 14 pages.
    DOI Scopus2
    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 Scopus9 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 Scopus2
    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 Scopus11 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 Scopus17 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 Scopus25 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 Scopus14 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 Scopus19 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 Scopus13 WoS7 Europe PMC4
    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 Scopus11 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 Scopus28 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 Scopus38 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 Scopus58 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 Scopus107 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 Scopus67 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 Scopus77 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 Scopus216 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 Scopus79 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). Shenzhen, China: 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). Glasgow, UK: IEEE.
    DOI Scopus11 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. 1-6). IEEE.
    DOI Scopus6
    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 Scopus78 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 Vol. 14 (pp. 6 pages). ITALY, Naples: IEEE.
    DOI Scopus11 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
Connect With Me
External Profiles