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.

I am a lecturer (teaching-research balanced) at the University of Adelaide (UoA) and a researcher at the Australian Institute for Machine Learning (AIML). In the same time, I am a Visiting Scholar at the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). Before joining UoA, I worked as a Laureate Postdoctoral Research Associate at AAII for three years and as a Research Fellow at RMIT University for one and a half years. I earned my PhD from the University of Technology Sydney in 2020 under the supervision of Distinguished Professor Jie Lu AO (Officer of the Order of Australia) after completing my Master and Bachelor in Statistics in China.

As an educator, I specialise in artificial intelligence and machine learning. I current teach and coordinate course such as Concepts in Artificial Intelligence and Using Machine Learning Tools PG. I also supervise course-based projects across various student cohorts, including Master of Computing & Innovation Project (PG), Artificial Intelligence and Machine Learning Research Project Part A and Part B (PG) and (Advanced) Topics in Computer Sciences (UG). My teaching philosophy is ""Think creatively and critically, Learn actively and persistently and Distinguish Yourself in teamwork".

As a researcher, my focus lies in addressing data distribution discrepancy issues in machine learning algorithms—one of the most fundamental challenges impacting model performance in terms of accuracy, robustness, fairness, and transparency. My research interests include:
· Robust Modeling under Distributional Discrepancies (detect and finetune)
· Multiple AI Agent Collaborations (workflow optimisation)

My methodologies have been successfully integrated or applied into a range of domain-specific learning scenarios, including:
· AI-empowered Collaborations in Mixed Reality Systems, in partnership with UniSA Australian Research Centre for Interactive and Virtual Environments
· AI Explainability and Transparency in collaboration with RMIT Enterprise AI and Data Analytics Hub
· AI for Biomedical, with UTS Institute for Biomedical Materials and Devices
· Real-time Forecasting Applications including weather and finance

In the long term, I aim to develop end-to-end trustworthy AI tools that support a wide range of users—from algorithm designers to system-level practitioners.

Opportunities
I am currently open to the following collaboration and recruitment opportunities:
· PhD Recruitment: I welcome applications from self-motivated PhD candidates (up to two per year), particularly in LLM fine-tuning and collaborative AI agents.
· Tutor Recruitment: Seeking responsible and passionate tutors to join my teaching team.
· Project-based Recruitment: Opportunities for Honours and Master’s students to engage in research-driven projects.

My research focuses on non-i.i.d (neither independently nor identically distributed) data, particularly addressing data distribution discrepancy issues in machine learning algorithms. 

My leading research topics including:

  • Robust Modeling under Distributional Discrepancies (detect and finetune)
  • Multiple AI Agent Collaborations (workflow optimisation)

Research topics in collaboration with others:

In the long term, I aim to develop end-to-end trustworthy AI tools that support a wide range of users—from algorithm designers to system-level practitioners.
 

  • Appointments
    Date Position Institution name
    2024 - ongoing Lecturer The University of Adelaide
    2023 - 2023 Research Fellow RMIT University
    2023 - ongoing Visting Scholar University of Technology
    2020 - 2023 Laureate Postdoctoral Research Associate University of Technology Sydney

  • 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
    Artificial Intelligence Machine learning Time-Series Analysis

  • 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
    2025 Ma, W., Wang, D., Song, Y., Xue, M., Wen, S., Li, Z., & Xiang, Y. (2025). TrapNet: Model Inversion Defense via Trapdoor. IEEE Transactions on Information Forensics and Security, 1.
    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), 1-16.
    DOI Scopus4
    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 Scopus15 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 Scopus20 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 Scopus20 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 Scopus12 WoS7 Europe PMC1
    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 Scopus23 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 Scopus16 WoS7 Europe PMC7
    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 Scopus31 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 Scopus14 WoS9 Europe PMC3
    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 Scopus30 WoS21 Europe PMC9
    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 Scopus43 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 Scopus64 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 Scopus75 WoS40 Europe PMC16
    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 Scopus82 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 Scopus229 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 Scopus89 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.
    Scopus2
    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
  • Surrey-Adelaide Partnership Fund - Chief Investigator (2025-2025)
  • ADMA 2024 Travel Grant
  • 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

2025

  • COMPSCI 7317/7617 Using Machine Learning Tools PG
  • COMPSCI7317OL - Using Machine Learning Tools
  • COMPSCI 7327 Concepts in Artificial Intelligence and Machine Learning
  • COMPSCI 2008 Advanced Topics in Computer Science
  • COMPSCI 7098 Master of Computing & Innovation Project
  • COMPSCI 7205A Artificial Intelligence and Machine Learning Research Project Part A
  • COMPSCI 7205B Artificial Intelligence and Machine Learning Research Project Part B

2024

  • COMPSCI 7327 Concepts in Artificial Intelligence and Machine Learning
  • 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