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
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Appointments
Date Position Institution name 2024 - ongoing Lecturer The University of Adelaide 2022 - ongoing Visting Scholar University of Technology -
Language Competencies
Language Competency Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title University of Technology Sydney Australia PhD -
Research Interests
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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.
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.
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.
Scopus4 WoS12023 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.
Scopus12023 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.
Scopus6 WoS22023 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.
Scopus13 WoS72022 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.
Scopus19 WoS112022 Yu, E., Song, Y., Zhang, G., & Lu, J. (2022). Learn-to-adapt: Concept drift adaptation for hybrid multiple streams. Neurocomputing, 496, 121-130.
Scopus10 WoS62022 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.
Scopus9 WoS72022 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.
Scopus18 WoS132022 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.
Scopus8 WoS7 Europe PMC32021 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.
Scopus8 WoS9 Europe PMC12021 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.
Scopus23 WoS21 Europe PMC52020 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.
Scopus32 WoS1032020 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.
Scopus50 WoS412017 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.
Scopus102 WoS882017 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.
Scopus59 WoS40 Europe PMC132016 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.
Scopus10 WoS72016 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.
Scopus73 WoS602016 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.
Scopus24 WoS182016 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.
Scopus9 WoS72016 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.
Scopus204 WoS1752015 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.
Scopus45 WoS382015 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.
Scopus72 WoS672015 Qin, S., Liu, F., Wang, J., & Song, Y. (2015). Interval forecasts of a novelty hybrid model for wind speeds. Energy Reports, 1, 8-16.
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.
Scopus32020 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.
Scopus9 WoS112019 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.
Scopus52018 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.
Scopus32017 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.
Scopus67 WoS272017 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.
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
Connect With Me
External Profiles