Dr Lia Song
Lecturer
School of Computer Science and Information Technology
College of Engineering and Information 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. For example, detecting whether text is generated by AI.
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:
- 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.
| 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 | Competency |
|---|---|
| Chinese (Mandarin) | Can read, write, speak, understand spoken and peer review |
| English | Can read, write, speak, understand spoken and peer review |
| Date | Institution name | Country | Title |
|---|---|---|---|
| University of Technology Sydney | Australia | PhD |
| Year | Citation |
|---|---|
| 2026 | Liu, M., Yu, X., Xu, C., & Song, Y. (2026). Preface. Lecture Notes in Computer Science, 16370 LNAI, v-vi. |
| 2026 | Liu, M., Yu, X., Xu, C., & Song, Y. (2026). Preface. Lecture Notes in Computer Science, 16371 LNAI, v-vi. |
| 2025 | Hu, X., Song, Y., & Zhong, A. (2025). Machine learning in the Australian equity market. PACIFIC-BASIN FINANCE JOURNAL, 94, 17 pages. |
| 2025 | Zhang, B., Lu, J., Song, Y., & Zhang, G. (2025). A Multistream Concept Drift Handling Framework via Data Sharing. IEEE Transactions on Cybernetics, 55(12), 1-12. |
| 2025 | Chu, R., Chik, L., Song, Y., Chan, J., & Li, X. (2025). Real-time fuel leakage detection via online change point detection. International Journal of Data Science and Analytics, 20(7), 6583-6600. |
| 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. |
| 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, 20, 1. Scopus5 WoS5 |
| 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. Scopus14 WoS10 |
| 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. Scopus26 WoS22 |
| 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. Scopus28 WoS24 Europe PMC4 |
| 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. Scopus25 WoS21 |
| 2022 | Yu, E., Song, Y., Zhang, G., & Lu, J. (2022). Learn-to-adapt: Concept drift adaptation for hybrid multiple streams. Neurocomputing, 496, 121-130. Scopus28 WoS25 |
| 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. Scopus19 WoS17 Europe PMC2 |
| 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. Scopus32 WoS29 Europe PMC1 |
| 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. Scopus21 WoS17 Europe PMC11 |
| 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. Scopus44 WoS32 |
| 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. Scopus18 WoS16 Europe PMC10 |
| 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. Scopus33 WoS31 Europe PMC17 |
| 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. Scopus49 WoS157 |
| 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. Scopus80 WoS67 |
| 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. Scopus114 WoS96 |
| 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. Scopus87 WoS64 Europe PMC25 |
| 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. 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. Scopus84 WoS70 |
| 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. Scopus27 WoS20 |
| 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. 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. Scopus248 WoS218 |
| 2015 | Qin, S., Liu, F., Wang, C., Song, Y., & Qu, J. (2015). Spatial-temporal analysis and projection of extreme particulate matter (PM10 and PM2.5) levels using association rules: A case study of the Jing-Jin-Ji region, China. Atmospheric Environment, 120, 339-350. Scopus48 WoS42 |
| 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. Scopus85 WoS78 |
| 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. Scopus51 WoS49 |
| Year | Citation |
|---|---|
| 2026 | Li, J., Song, Y., & Liu, L. (2026). Predicting Generalization Error Under Graph Distribution Shifts via Parameter Discrepancy with Accumulated Gradient. In Lecture Notes in Computer Science Vol. 16370 LNAI (pp. 336-350). Springer Nature Singapore. DOI |
| 2025 | Song, Y., Yuan, Z., Zhang, S., Fang, Z., Yu, J., & Liu, F. (2025). DEEP KERNEL RELATIVE TEST FOR MACHINE-GENERATED TEXT DETECTION. In 13th International Conference on Learning Representations Iclr 2025 (pp. 100560-100586). Scopus1 |
| 2025 | Bai, J., Song, Y., Wu, D., Sajjanhar, A., Xiang, Y., Zhou, W., . . . Li, Y. (2025). A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Vol. 2 (pp. 71-82). CANADA, Toronto: ASSOC COMPUTING MACHINERY. DOI Scopus1 |
| 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 Scopus7 WoS5 |
| 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 Scopus14 WoS14 |
| 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 WoS2 |
| 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 WoS2 |
| 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 Scopus105 WoS71 |
| 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 Scopus14 WoS13 |
| 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. Scopus7 |
| 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. Scopus9 |
- 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
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2025 | Principal Supervisor | Enhancing Feature Alignment in Multimodal Models through Mathematical Analysis | Master of Philosophy | Master | Full Time | Mr Haowen Pan |
| 2025 | Principal Supervisor | Multi-agent Vision-and-Language Navigation Base on Large Foundation Models | Doctor of Philosophy | Doctorate | Full Time | Mr Qunchao Jin |
| 2025 | Principal Supervisor | Explainable AI for Individual-level Trustworthiness in Decision Making Systems | Doctor of Philosophy | Doctorate | Full Time | Mr Zhenlin Xu |
| 2025 | Co-Supervisor | Improving the Automation Efficiency of Q&A Chatbots Based on AI Agents | Master of Philosophy | Master | Full Time | Ms Shiya Huang |
| 2025 | Principal Supervisor | Multi-agent Vision-and-Language Navigation Base on Large Foundation Models | Doctor of Philosophy | Doctorate | Full Time | Mr Qunchao Jin |
| 2025 | Principal Supervisor | Explainable AI for Individual-level Trustworthiness in Decision Making Systems | Doctor of Philosophy | Doctorate | Full Time | Mr Zhenlin Xu |
| 2025 | Co-Supervisor | Improving the Automation Efficiency of Q&A Chatbots Based on AI Agents | Master of Philosophy | Master | Full Time | Ms Shiya Huang |
| 2025 | Principal Supervisor | Enhancing Feature Alignment in Multimodal Models through Mathematical Analysis | Master of Philosophy | Master | Full Time | Mr Haowen Pan |
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