Lin Liu

Research Interests

Artificial Intelligence Bioinformatics and computational biology Data mining and knowledge discovery Health Informatics Learning analytics Machine learning

Prof Lin Liu

Professor

School of Computer Science and Information Technology

College of Engineering and Information Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Professor Lin Liu's research interest is in the fields of machine learning and data mining, with the focus on developing robust machine learning/data mining algorithms by utilising causal relationships discovered from big data. Her resesarch is supported by the Australian Research Council (ARC) and the Cooperative Research Centre (CRC) program, as well as industry research funding. Lin also works closely with experts in other areas (e.g. cancer biology, marketing and eduction) to develop and apply data mining/machine learning and causal inference methods to solve real world problems.
 
Lin has been supervising PhD students in data mining, machine learning and bioinformatics, and she has taught a broad range of courses in computer science, including computer networking, databases, artificial intelligence, programming, software engineering and business processing modelling courses. Currently she is teaching and developing courses in machine learning and artificial intelligence.

  • Data mining
  • machine learning
  • Causal inference and causal discovery
  • Bioinformatics

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
1999 - 2006 University of South Australia Australia PhD

Year Citation
2026 Liu, C., Feng, Z., Deng, Z., Liu, L., Li, J., Zhai, R., . . . Qin, L. (2026). Counterfactual samples constructing and training for commonsense statements estimation. Information Processing and Management, 63(3), 16 pages.
DOI
2026 Wang, K., Zhang, Y., Zhang, H. Y., Liu, L., Li, J., Feng, Z., & Cheng, D. (2026). Learning fair representation for fine-tuning pre-trained language models. Neural Networks the Official Journal of the International Neural Network Society, 198, 108701.
DOI
2026 Chen, Q., Zeng, H., Cheng, D., Li, J., Liu, L., & Zhang, S. (2026). Towards robust graph-level anomaly detection via counterfactual augmentation. Knowledge Based Systems, 334, 115138.
DOI
2026 Huang, Z., Zhang, S., Cheng, D., Li, J., Liu, L., Lu, G., & Zhang, G. (2026). Learning instrumental variable representation for debiasing in recommender systems. Neural Networks, 193(107977), 1-13.
DOI Scopus7 WoS4
2026 Zhang, Z., Zhou, J., Yao, J., Liu, L., Li, J., Li, L., & Wu, X. (2026). Learning label-specific features for multi-dimensional classification. Pattern Recognition, 172(112365), 1-14.
DOI
2026 Chen, Q., Deng, J., Cheng, D., Li, J., & Liu, L. (2026). Multi-view debiasing representation learning for recommender systems. Information Processing and Management, 63(2), 1-18.
DOI
2026 Yang, J., Wang, X., Liu, L., & Li, J. (2026). A bilevel meta-task correlation network for bearings remaining useful life prediction with limited data. Engineering Applications of Artificial Intelligence, 175, 14 pages.
DOI
2026 Zhang, Z., Chang, Y., Yao, J., Liu, L., Li, J., & Wu, X. (2026). Exploiting Global Information for Partial Multi-Label Learning. IEEE Transactions on Knowledge and Data Engineering, 1-14.
DOI
2026 Lu, S., Liu, L., Yu, K., Le, T. D., Liu, J., & Li, J. (2026). Dependency-based anomaly detection: A general framework and comprehensive evaluation. Expert Systems with Applications, 297(129249), 129249.
DOI Scopus1 WoS1
2025 Yang, F., Liu, J., Li, J., Liu, L., Wang, S., Li, W., & Ni, S. (2025). Causal reinforcement learning for train scheduling on single-track railway networks. Transportation Research Part C Emerging Technologies, 178(105215), 105215.
DOI
2025 Deho, O. B., Bewong, M., Kwashie, S., Li, J., Liu, J., Liu, L., & Joksimovic, S. (2025). Is it still fair? A comparative evaluation of fairness algorithms through the lens of covariate drift. Machine Learning, 114(1), 1-19.
DOI Scopus1 WoS1
2025 Pinero, S., Li, X., Zhang, J., Winter, M., Lee, S. H., Nguyen, T., . . . Le, T. D. (2025). Omics-based computational approaches for biomarker identification, prediction, and treatment of Long COVID. Critical Reviews in Clinical Laboratory Sciences, 1-27.
DOI Scopus1 WoS1
2025 Cheng, D., Li, J., Liu, L., Xu, Z., Zhang, W., Liu, J., & Le, T. D. (2025). Disentangled Representation Learning for Causal Inference With Instruments. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14078-14091.
DOI Scopus7 WoS7
2025 Li, X., Liu, L., Li, J., & Le, T. D. (2025). Stable Breast Cancer Prognosis. IEEE Transactions on Computational Biology and Bioinformatics, 22(2), 721-731.
DOI Scopus1
2025 Furqon, M. T., Pratama, M., Shiddiqi, A., Liu, L., Habibullah, H., & Dogancay, K. (2025). Time and frequency synergy for source-free time-series domain adaptations. Information Sciences, 695(121734), 20 pages.
DOI Scopus8 WoS4
2025 Peng, F., Little, K., & Liu, L. (2025). Human-centred design (HCD) in enhancing dementia care through assistive technologies: a scoping review. Digital, 5(4, article no. 51), 1-22.
DOI
2025 Huang, Z., Hu, Y., Cheng, D., Li, J., Liu, L., Zhang, G., & Zhang, S. (2025). Multi-cause deconfounding for recommender systems with latent confounders. Knowledge-Based Systems, 329(114345), 1-14.
DOI Scopus7 WoS6
2025 Cifuentes Bernal, A. M., Liu, L., Li, J., & Le, T. D. (2025). Identifying cooperative genes causing cancer progression with dynamic causal inference. Royal Society Open Science, 12(12), 250442-1-250442-20.
DOI
2025 Pinero, S., Li, X., Liu, L., Li, J., Lee, S. H., Winter, M., . . . Le, T. D. (2025). Integrative multi-omics framework for causal gene discovery in Long COVID. PLoS Computational Biology, 21(12), e1013725-1-e1013725-32.
DOI
2025 Zhang, Y., Xu, T., Cheng, D., Li, J., Liu, L., Xu, Z., & Feng, Z. (2025). Data-driven learning optimal K values for K-nearest neighbour matching in causal inference. Data Mining And Knowledge Discovery, 39(4, article no. 35), 1-24.
DOI Scopus1 WoS1
2025 Yang, J., Wang, X., Zhang, M., Liu, L., & Li, J. (2025). Meta-knowledge random attention update network for few-shot and anti-noise remaining useful life prediction. Advanced Engineering Informatics, 65(103358), 1-13.
DOI Scopus4 WoS4
2025 Yang, J., Wang, X., Zhang, M., Liu, L., & Li, J. (2025). Adaptive dynamic causal meta graph-task network for remaining useful life prediction with extreme long-tailed distribution condition. IEEE Transactions On Industrial Informatics, online(9), 1-11.
DOI Scopus1 WoS1
2025 Ma'sum, M. A., Pratama, M., Lughofer, E., Liu, L., Habibullah., & Kowalczyk, R. (2025). Few-shot continual learning via flat-to-wide approaches. IEEE Transactions on Neural Networks and Learning Systems, 36(5), 8966-8978.
DOI Scopus2 WoS4 Europe PMC1
2025 Lu, S., Liu, L., Li, J., Chambers, J., Cook, M. J., & Grayden, D. B. (2025). Leveraging channel coherence in long-term iEEG data for seizure prediction. IEEE Journal of Biomedical and Health Informatics, online(8), 1-8.
DOI Scopus1 WoS1
2025 Zhang, G., Yuan, G., Cheng, D., Liu, L., Li, J., Xu, Z., & Zhang, S. (2025). Deconfounding representation learning for mitigating latent confounding effects in recommendation. Knowledge and Information Systems, 67(7), 5999-6020.
DOI Scopus11 WoS9
2025 Zhang, G., Yuan, G., Cheng, D., Liu, L., Li, J., & Zhang, S. (2025). Mitigating propensity bias of large language models for recommender systems. ACM Transactions on Information Systems, 43(6, article no. 150), 1-26.
DOI Scopus34 WoS22
2024 Guo, X., Yu, K., Liu, L., Li, J., Liang, J., Cao, F., & Wu, X. (2024). Progressive skeleton learning for effective local-to-global causal structure learning. IEEE Transactions on Knowledge and Data Engineering, 36(12), 9065-9079.
DOI Scopus7 WoS5
2024 Zhang, G., Yuan, G., Cheng, D., Liu, L., Li, J., & Zhang, S. (2024). Disentangled contrastive learning for fair graph representations. Neural Networks, 181(106781), 1-11.
DOI Scopus49 WoS45 Europe PMC2
2024 Yu, K., Ling, Z., Liu, L., Li, P., Wang, H., & Li, J. (2024). Feature selection for efficient local-to-global Bayesian network structure learning. ACM Transactions on Knowledge Discovery from Data, 18(2), 1-27.
DOI Scopus12 WoS6
2024 Furqon, M., Pratama, M., Liu, L., Habibullah, H., & Dogancay, K. (2024). Mixup domain adaptations for dynamic remaining useful life predictions. Knowledge-based Systems, 295(111783), 1-13.
DOI Scopus21 WoS20
2024 Guo, X., Yu, K., Liu, L., Cao, F., & Li, J. (2024). Causal feature selection with dual correction. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 938-951.
DOI Scopus17 WoS17
2024 Zhang, Z., Yao, J., Liu, L., Li, J., Li, L., & Wu, X. (2024). Partial label feature selection: an adaptive approach. IEEE Transactions on Knowledge and Data Engineering, 36(8), 4178-4191.
DOI Scopus11 WoS9
2024 Ye, W., Li, C., Zhang, W., Li, J., Liu, L., Cheng, D., & Feng, Z. (2024). Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions. Methods, 231, 15-25.
DOI Scopus2 WoS2 Europe PMC2
2024 Zhang, J., Liu, L., Wei, X., Zhao, C., Luo, Y., Li, J., & Le, T. D. (2024). Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data. BMC Biology, 22(1), 19 pages.
DOI Scopus6 WoS6 Europe PMC5
2024 Cheng, D., Li, J., Liu, L., Yu, K., Duy Le, T., & Liu, J. (2024). Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data. IEEE Transactions on Neural Networks and Learning Systems, 35(8), 11542-11552.
DOI Scopus10 WoS8 Europe PMC2
2024 Deho, O. B., Liu, L., Li, J., Liu, J., Zhan, C., & Joksimovic, S. (2024). When the Past != The Future: Assessing the Impact of Dataset Drift on the Fairness of Learning Analytics Models. IEEE Transactions on Learning Technologies, 17, 1007-1020.
DOI Scopus8 WoS4
2024 Ma'sum, M. A., Sarkar, M. D. R., Pratama, M., Ramasamy, S., Anavatti, S., Liu, L., . . . Kowalczyk, R. (2024). Dynamic long-term time-series forecasting via meta transformer networks. IEEE Transactions on Artificial Intelligence, 5(8), 4258-4581.
DOI Scopus2
2024 Masum, M. A., Pratama, M., Ramasamy, S., Liu, L., & Habibullah, H. (2024). Unsupervised few-shot continual learning for remote sensing image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 62(4707214), 1-14.
DOI Scopus6 WoS4
2024 Cheng, D., Jiuyong, L. I., Liu, L., Liu, J., & Thuc Duy, L. E. (2024). Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey. ACM Computing Surveys, 56(5), 37 pages.
DOI Scopus36 WoS32
2024 Liu, J., Li, J., Liu, L., Le, T., Ye, F., & Li, G. (2024). Fairmod: making predictions fair in multiple protected attributes. Knowledge and Information Systems, 66(3), 1861-1884.
DOI Scopus3 WoS3
2023 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2023). Personalized Interventions to Increase the Employment Success of People With Disability. IEEE Transactions on Big Data, 9(6), 1561-1574.
DOI Scopus1 WoS1
2023 Li, J., Liu, L., Zhang, S., Ma, S., Le, T. D., & Liu, J. (2023). Causal heterogeneity discovery by bottom-up pattern search for personalised decision making. Applied Intelligence, 53(7), 8180-8194.
DOI
2023 Cheng, D., Li, J., Liu, L., Zhang, J., Liu, J., & Le, T. D. (2023). Local Search for Efficient Causal Effect Estimation. IEEE Transactions on Knowledge and Data Engineering, 35(9), 8823-8837.
DOI Scopus16 WoS16
2023 Bewong, M., Wondoh, J., Kwashie, S., Liu, J., Liu, L., Li, J., . . . Kernot, D. (2023). DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text. IEEE Access, 11, 32826-32841.
DOI Scopus4 WoS3
2023 Cheng, D., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2023). Toward unique and unbiased causal effect estimation from data with hidden variables. IEEE Transactions on Neural Networks and Learning Systems, 34(9), 1-13.
DOI Scopus24 WoS26 Europe PMC5
2023 Zhang, J., Liu, L., Wei, X., Zhao, C., Li, S., Li, J., & Le, T. D. (2023). Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers. Plos Computational Biology, 19(10 October), 28 pages.
DOI Scopus5 WoS4 Europe PMC5
2023 Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023). Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why. IEEE Transactions on Learning Technologies, 16(4), 560-572.
DOI Scopus20 WoS15
2023 Zhang, Z., Liu, L., Li, J., & Wu, X. (2023). Integrating global and local feature selection for multi-label learning. ACM Transactions on Knowledge Discovery from Data, 17(1, article no. 4), 1-37.
DOI Scopus18 WoS16
2023 Yu, K., Cai, M., Wu, X., Liu, L., & Li, J. (2023). Multilabel feature selection: a local causal structure learning approach. IEEE Transactions on Neural Networks and Learning Systems, 34(6), 3044-3057.
DOI Scopus33 WoS31 Europe PMC5
2023 Yang, S., Yu, K., Cao, F., Liu, L., Wang, H., & Li, J. (2023). Learning causal representations for robust domain adaptation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2750-2764.
DOI Scopus39 WoS36
2023 Guo, X., Yu, K., Liu, L., Li, P., & Li, J. (2023). Adaptive Skeleton construction for accurate DAG Learning. IEEE Transactions on Knowledge and Data Engineering, 35(10), 10536-10539.
DOI Scopus27 WoS22
2023 Zhang, Z., Zhang, Z., Yao, J., Liu, L., Li, J., Wu, G., & Wu, X. (2023). Multi-label feature selection via adaptive label correlation estimation. ACM Transactions on Knowledge Discovery from Data, 17(9), 1-28.
DOI Scopus27 WoS15
2022 Zhang, W., Li, J., & Liu, L. (2022). A unified survey of treatment effect heterogeneity modelling and uplift modelling. ACM Computing Surveys, 54(8, article no. 162), 1-36.
DOI Scopus55 WoS52
2022 Ling, Z., Yu, K., Liu, L., Li, J., Zhang, Y., & Wu, X. (2022). PSL: An algorithm for partial Bayesian network structure learning. ACM Transactions on Knowledge Discovery from Data, 16(5, article no. 93), 1-25.
DOI Scopus11 WoS10
2022 Ling, Z., Yu, K., Zhang, Y., Liu, L., & Li, J. (2022). Causal learner: a toolbox for causal structure and Markov blanket learning. Pattern Recognition Letters, 163, 92-95.
DOI Scopus31 WoS28
2022 Zamecnik, A., Kovanović, V., Joksimović, S., & Liu, L. (2022). Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study. Computers and Education Artificial Intelligence, 3(100051), 100051.
DOI Scopus47
2022 Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Le, T. D. (2022). How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?. British Journal of Educational Technology, 53(4), 822-843.
DOI Scopus65 WoS47
2022 Zhang, J., Liu, L., Zhang, W., Li, X., Zhao, C., Li, S., . . . Le, T. D. (2022). MiRspongeR 2.0: An enhanced R package for exploring miRNA sponge regulation. Bioinformatics Advances, 2(1), 3 pages.
DOI Scopus3 WoS2 Europe PMC5
2022 Cifuentes Bernal, A. M., Pham, V. V. H., Li, X., Liu, L., Li, J., & Le, T. D. (2022). Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories. Briefings in Functional Genomics, 21(6), 455-465.
DOI Scopus3 WoS4 Europe PMC3
2022 Li, X., Liu, L., Whitehead, C., Li, J., Thierry, B., Le, T. D., & Winter, M. (2022). Identifying preeclampsia-associated genes using a control theory method. Briefings in Functional Genomics, 21(4), 296-309.
DOI Scopus5 WoS6 Europe PMC9
2022 Zhang, J., Liu, L., Xu, T., Zhang, W., Li, J., Rao, N., & Le, T. D. (2022). Time to infer miRNA sponge modules. Wiley Interdisciplinary Reviews RNA, 13(2), 21 pages.
DOI Scopus20 WoS20 Europe PMC18
2022 Liu, J., Li, J., & Liu, L. (2022). FastOPM—A practical method for partial match of time series. Pattern Recognition, 130(article no. 108808), 12 pages.
DOI
2022 Cheng, D., Li, J., Liu, L., Le, T. D., Liu, J., & Yu, K. (2022). Sufficient dimension reduction for average causal effect estimation. Data Mining and Knowledge Discovery, 36(3), 1174-1196.
DOI Scopus11 WoS10
2021 Li, J., Zhang, W., Liu, L., Yu, K., Le, T. D., & Liu, J. (2021). A general framework for causal classification. International Journal of Data Science and Analytics, 11(2), 127-139.
DOI Scopus6 WoS6
2021 Li, X., Truong, B., Xu, T., Liu, L., Li, J., & Le, T. D. (2021). Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis. BMC Bioinformatics, 22(1), 22 pages.
DOI Scopus6 WoS6 Europe PMC3
2021 Zhang, J., Liu, L., Xu, T., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2021). miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data. RNA Biology, 18(12), 2308-2320.
DOI Scopus10 WoS7 Europe PMC8
2021 Pham, V. V. H., Li, X., Truong, B., Nguyen, T., Liu, L., Li, J., & Le, T. D. (2021). The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge. Briefings in Bioinformatics, 22(3), 4 pages.
DOI Scopus1 WoS2 Europe PMC4
2021 Zhang, J., Liu, L., Xu, T., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2021). Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data. BMC Bioinformatics, 22(1), 19 pages.
DOI Scopus17 WoS14 Europe PMC16
2021 Cifuentes-Bernal, A. M., Pham, V. V., Li, X., Liu, L., Li, J., & Le, T. D. (2021). A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes. Bioinformatics, 37(6), 807-814.
DOI Scopus6 WoS4 Europe PMC4
2021 Pham, V. V. H., Liu, L., Bracken, C., Goodall, G., Li, J., & Le, T. D. (2021). Computational methods for cancer driver discovery: A survey. Theranostics, 11(11), 5553-5568.
DOI Scopus22 WoS22 Europe PMC15
2021 Pham, V. V. H., Liu, L., Bracken, C. P., Nguyen, T., Goodall, G. J., Li, J., & Le, T. D. (2021). pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers. Bioinformatics, 37(19), 3285-3292.
DOI Scopus13 WoS13 Europe PMC11
2021 Yu, K., Liu, L., & Li, J. (2021). A unified view of causal and non-causal feature selection. ACM Transactions on Knowledge Discovery from Data, 15(4, article no. 3436891), 1-46.
DOI Scopus90 WoS82
2020 Yu, K., Guo, X., Liu, L., Li, J., Wang, H., Ling, Z., & Wu, X. (2020). Causality-based feature selection: methods and evaluations. ACM Computing Surveys, 53(5, article no. 111), 1-36.
DOI Scopus186 WoS168
2020 Yu, K., Liu, L., & Li, J. (2020). Learning Markov blankets from multiple interventional data sets. IEEE Transactions on Neural Networks and Learning Systems, 31(6), 2005-2019.
DOI Scopus25 WoS21 Europe PMC3
2020 Li, X., Liu, L., Goodall, G. J., Schreiber, A., Xu, T., Li, J., & Le, T. D. (2020). A novel single-cell based method for breast cancer prognosis. PLoS Computational Biology, 16(8), e1008133-1-e1008133-20.
DOI Scopus23 WoS18 Europe PMC21
2020 Pham, V. V. H., Liu, L., Bracken, C. P., Goodall, G. J., Li, J., & Le, T. D. (2020). DriverGroup: a novel method for identifying driver gene groups. Bioinformatics, 36(Supplement_2), i583-i591.
DOI Scopus7 WoS8 Europe PMC5
2020 Zhan, C., Roughead, E., Liu, L., Pratt, N., & Li, J. (2020). Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data. Journal of Biomedical Informatics, 112(article no. 103603), 13 pages.
DOI Scopus15 WoS13 Europe PMC10
2020 Zhan, C., Roughead, E., Liu, L., Pratt, N., & Li, J. (2020). Detecting potential signals of adverse drug events from prescription data. Artificial Intelligence in Medicine, 104(101839), 14 pages.
DOI Scopus12 WoS9 Europe PMC9
2020 Zhang, J., Xu, T., Liu, L., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2020). LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. Plos Computational Biology, 16(4), e1007851.
DOI Scopus22 Europe PMC23
2020 Yu, K., Liu, L., Li, J., Ding, W., & Le, T. D. (2020). Multi-Source Causal Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2240-2256.
DOI Scopus118 WoS96 Europe PMC9
2020 Li, J., Liu, L., Le, T. D., & Liu, J. (2020). Accurate data-driven prediction does not mean high reproducibility. Nature Machine Intelligence, 2(1), 13-15.
DOI Scopus42 WoS40
2020 Zhang, J., Xu, T., Liu, L., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2020). LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLOS COMPUTATIONAL BIOLOGY, 16(4), 22 pages.
DOI WoS21
2020 Ansah, J., Liu, L., Kang, W., Liu, J., & Li, J. (2020). Leveraging burst in twitter network communities for event detection. World Wide Web, 23(5), 2851-2876.
DOI Scopus21 WoS15
2020 Zhang, J., Pham, V. V. H., Liu, L., Xu, T., Truong, B., Li, J., . . . Le, T. D. (2020). Correction: Identifying mirna synergism using multiple-intervention causal inference (BMC Bioinformatics (2019) 20 (613) DOI: 10.1186/s12859-019-3215-5). BMC Bioinformatics, 21(1), 2 pages.
DOI
2019 Le, T. D., Zhao, Y., Wong, S., Jin, W. H., Ong, K. L., Liu, L., & Williams, G. (2019). Preface. Communications in Computer and Information Science, 1127 CCIS, v-vi.
2019 Bewong, M., Liu, J., Liu, L., & Li, J. (2019). Privacy preserving serial publication of transactional data. Information Systems, 82, 53-70.
DOI Scopus12 WoS10
2019 Ma, S., Li, J., Liu, L., & Le, T. D. (2019). Discovering context specific causal relationships. Intelligent Data Analysis, 23(4), 917-931.
DOI
2019 Ma, S., Liu, L., Li, J., & Le, T. D. (2019). Data-driven discovery of causal interactions. International Journal of Data Science and Analytics, 8(3), 285-297.
DOI Scopus2 WoS3
2019 Zhang, J., Liu, L., Xu, T., Xie, Y., Zhao, C., Li, J., & Le, T. D. (2019). MiRspongeR: An R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics, 20(1), 12 pages.
DOI Scopus35 WoS28 Europe PMC30
2019 Zhang, J., Pham, V. V. H., Liu, L., Xu, T., Truong, B., Li, J., . . . Le, T. D. (2019). Identifying miRNA synergism using multiple-intervention causal inference. BMC Bioinformatics, 20(1), 11 pages.
DOI Scopus14 WoS14 Europe PMC18
2019 Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., . . . Müller, F. (2019). Assessment of network module identification across complex diseases. Nature Methods, 16(9), 843-852.
DOI Scopus215 WoS209 Europe PMC230
2019 Bewong, M., Liu, J., Liu, L., Li, J., & Choo, K. K. R. (2019). A relative privacy model for effective privacy preservation in transactional data. Concurrency and computation: practice and experience, 31(23, article no. e4923), 1-13.
DOI Scopus8 WoS8
2019 Pham, V. V. H., Liu, L., Bracken, C. P., Goodall, G. J., Long, Q., Li, J., & Le, T. D. (2019). CBNA: a control theory based method for identifying coding and non-coding cancer drivers. PLoS computational biology, 15(12), e1007538.
DOI Scopus30 WoS31 Europe PMC21
2019 Pham, V. V., Zhang, J., Liu, L., Truong, B., Xu, T., Nguyen, T. T., . . . Le, T. D. (2019). Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction. BMC Bioinformatics, 20(1, article no. 143), 1-12.
DOI Scopus21 WoS24 Europe PMC21
2019 Ling, Z., Yu, K., Wang, H., Liu, L., Ding, W., & Wu, X. (2019). BAMB: A balanced markov blanket discovery approach to feature selection. ACM transactions on intelligent systems and technology, 10(5, article no. 52), 1-25.
DOI Scopus64 WoS57
2018 Zhang, Z., Liu, L., Wang, H., Li, J., Hu, D., Yan, J., . . . Meierer, M. (2018). Collective behavior learning by differentiating personal preference from peer influence. Knowledge-based systems, 159, 233-243.
DOI Scopus6 WoS5
2018 Yu, K., Liu, L., Li, J., & Chen, H. (2018). Mining Markov blankets without causal sufficiency. IEEE Transactions on neural networks and learning systems, 29(12), 6333-6347.
DOI Scopus41 WoS35 Europe PMC7
2018 Boo, Y. L., Stirling, D., Chi, L., Liu, L., Ong, K. L., & Williams, G. (2018). Preface. Communications in Computer and Information Science, 845, v-vi.
2018 Liu, L., Li, J., Zhang, K., Kıcıman, E., & Kiyavash, N. (2018). Guest editorial: special issue on causal discovery 2017. International Journal of Data Science and Analytics, 6(1), 1-2.
DOI Scopus1 WoS16
2018 Zhan, C., Roughead, E., Liu, L., Pratt, N., & Li, J. (2018). A data-driven method to detect adverse drug events from prescription data. Journal of Biomedical Informatics, 85, 10-20.
DOI Scopus9 WoS8 Europe PMC6
2018 Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D., & Long, Q. (2018). Predicting academic performance by considering student heterogeneity. Knowledge-Based Systems, 161, 134-146.
DOI Scopus168 WoS116
2018 Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., & Murray, D. J. (2018). Identifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, 7(3), 227-245.
DOI Scopus40 WoS30
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2018 Xu, T., Su, N., Liu, L., Zhang, J., Wang, H., Zhang, W., . . . Le, T. D. (2018). miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase. BMC Bioinformatics, 19(514), 1-10.
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2018 Kwashie, S., Liu, J., Li, J., Liu, L., Stumptner, M., & Yang, L. (2018). Certus: An effective entity resolution approach with graph differential dependencies (GDDs). Proceedings of the VLDB Endowment, 12(6), 653-666.
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2017 Xu, T., Le, T. D., Liu, L., Su, N., Wang, R., Sun, B., . . . Li, J. (2017). CancerSubtypes: An R/Bioconductor package for molecular cancer subtype identification, validation and visualization. Bioinformatics, 33(19), 3131-3133.
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2017 Zhang, J., Le, T. D., Liu, L., & Li, J. (2017). Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer. BMC Bioinformatics, 18(1), 12 pages.
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2017 Li, J., Ma, S., Le, T., Liu, L., & Liu, J. (2017). Causal Decision Trees. IEEE transactions on knowledge and data engineering, 29(2), 257-271.
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2017 Li, J., Liu, L., Liu, J., & Green, R. (2017). Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data. Health information science and systems, 5(5), 1-10.
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2017 Li, J., & Zhang, K. (2017). Guest Editorial: Special Issue on Causal Discovery 2017. International journal of data science and analytics, 3(2), 79-80.
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2017 Azfar, A., Choo, K. K. R., & Liu, L. (2017). Forensic taxonomy of android productivity apps. Multimedia tools and applications, 76(3), 3313-3341.
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2016 Azfar, A., Choo, K. K. R., & Liu, L. L. (2016). An android communication app forensic taxonomy. Journal of forensic sciences, 61(5), 1337-1350.
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2016 Azfar, A., Choo, R., & Liu, L. (2016). Android mobile VoIP apps: a survey and examination of their security and privacy. Electronic commerce research, 16(1), 73-111.
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2016 Zhang, H., Jin, W., Zhu, X., Liu, L., He, Z., Yang, S., . . . Liu, Y. (2016). Identification and characterization of Salvia miltiorrhizain miRNAs in response to replanting disease. Plos One, 11(8), e0159905.
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2016 Li, J., Le, T. D., Liu, L., Liu, J., Jin, Z., Sun, B., & Ma, S. (2016). From observational studies to causal rule mining. ACM transactions on intelligent systems and technology, 7(2, article no. 14), 1-27.
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2016 Xu, T., Le, T. D., Liu, L., Wang, R., Sun, B., & Li, J. (2016). Identifying cancer subtypes from miRNA-TFmRNA regulatory networks and expression data. Plos One, 11(4), 20 pages.
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2016 Masud Karim, S. M., Liu, L., Le, T. D., & Li, J. (2016). Identification of miRNA-mRNA regulatory modules by exploring collective group relationships. BMC Genomics, 17(1), 14 pages.
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2016 Zhang, J., Duy Le, T., Liu, L., He, J., & Li, J. (2016). Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs. Molecular Biosystems, 12(2), 454-463.
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2015 Le, T. D., Zhang, J., Liu, L., & Li, J. (2015). Ensemble methods for miRNA target prediction from expression data. Plos One, 10(6), 19 pages.
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2015 Le, T. D., Zhang, J., Liu, L., Liu, H., & Li, J. (2015). miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships. PLOS One, 10(12), 1-15.
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2014 Zhang, J., Thuc, D., Liu, L., Liu, B., He, J., Goodall, G., & Li, J. (2014). Identifying direct miRNA-mRNA causal regulatory relationships in heterogeneous data. Journal of Biomedical Informatics, 52, 438-447.
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2014 Zhang, J., Thuc, D., Liu, L., Liu, B., He, J., Goodall, G., & Li, J. (2014). Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data. Bioinformatics, 30(21), 3070-3077.
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2014 Le, T. D., Liu, L., Zhang, J., Liu, B., & Li, J. (2014). From miRNA regulation to miRNA-TF co-regulation: Computational approaches and challenges. Briefings in Bioinformatics, 16(3), 475-496.
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2014 Irwin, A. S. M., Slay, J., Choo, K. K. R., & Lui, L. (2014). Money laundering and terrorism financing in virtual environments: a feasibility study. Journal of money laundering control, 17(1), 50-75.
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2013 Irwin, A. S. M., Slay, J., Choo, K. K. R., & Liu, L. (2013). Are the financial transactions conducted inside virtual environments truly anonymous?: An experimental research from an Australian perspective. Journal of money laundering control, 16(1), 6-40.
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2013 Liu, H., Li, J., Liu, L., Liu, J., Lee, I., & Zhao, J. (2013). Exploring groups from heterogeneous data via sparse learning. Lecture notes in computer science, 7818 LNAI(PART 1), 556-567.
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2013 Liu, B., Liu, L., Tsykin, A., Goodall, G., Cairns, M., & Li, J. (2013). Discovering functional microRNAmRNA regulatory modules in heterogeneous data. Advances in Experimental Medicine and Biology, 774, 267-290.
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2013 Le, T., Liu, L., Liu, B., Tsykin, A., Goodall, G., Satou, K., & Li, J. (2013). Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics, 14(article no. 92), 1-13.
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2013 Le, T., Liu, L., Tsykin, A., Goodall, G., Liu, B., Sun, B., & Li, J. (2013). Inferring microRNA-mRNA causal regulatory relationships from expression data. Bioinformatics, 29(6), 765-771.
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2012 Irwin, A. S. M., Choo, K. K. R., & Liu, L. (2012). Modelling of money laundering and terrorism financing typologies. Journal of money laundering control, 15(3), 316-335.
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2012 Irwin, A. S. M., Choo, K. K., & Liu, L. (2012). An analysis of money laundering and terrorism financing typologies. Journal of money laundering control, 15(1), 85-111.
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2011 Zhang, Z., Liu, L., Li, J., & Zhang, Z. (2011). Spectral representation of protein sequences. Journal of computational and theoretical nanoscience, 8(7), 1335-1339.
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2010 Liu, B., Liu, L., Tsykin, A., Goodall, G., Green, J., Zhu, M., . . . Li, J. (2010). Identifying functional miRNA-mRNA regulatory module with correspondence latent dirichlet allocation. Bioinformatics, 26(24), 3105-3111.
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2008 Liu, L., Cozzolino, D., Cynkar, W., Dambergs, R., Janik, L., O'Neill, B., . . . Gishen, M. (2008). Preliminary study on the application of visible-near infrared spectroscopy and chemometrics to classify Riesling wines from different countries. Food Chemistry, 106(2), 781-786.
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2026 Vu, T., Le, T. D., Tran, H. X., Liu, L., Li, J., & Du, J. T. (2026). Causal Recommendation Method for Personalised Chemotherapy Optimisation in Breast Cancer. In Communications in Computer and Information Science (Vol. 2765 CCIS, pp. 397-411). Springer Nature Singapore.
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2025 Gao, W., Xu, Z., Li, J., Liu, L., Liu, J., Le, T. D., . . . Chen, Y. (2025). TSI: A Multi-view Representation Learning Approach for Time Series Forecasting. In M. Gong, Y. Song, Y. S. Koh, W. Xiang, & D. Wang (Eds.), Event/exhibition information: 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, Melbourne, Australia, 25/11/2024-29/11/2024
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2025 Dzakpasu, D. Q., Liu, J., Li, J., & Liu, L. (2025). Improving Intersectional Group Fairness Using Conditional Generative Adversarial Network and Transfer Learning. In Event/exhibition information: 37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024, Melbourne, Australia, 25/11/2024-29/11/2024
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2024 Park, J. Y., Liu, L., Liu, J., & Li, J. (2024). Causal Disentanglement for Adversarial Defense. In Event/exhibition information: 36th Australasian Joint Conference on Artificial Intelligence, AJCAI 2023, Brisbane, Australia, 28/11/2023 - 01/12/2023
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2023 Xu, Z., Liu, J., Cheng, D., Li, J., Liu, L., & Wang, K. (2023). Disentangled Representation with Causal Constraints for Counterfactual Fairness. In Event/exhibition information: 27th Pacific-Asia Conferenceon Knowledge Discovery and Data Mining (PAKDD 2023), Osaka, Japan, 25/05/2023-28/05/2023
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2023 Xu, T., Zhang, Y., Li, J., Liu, L., Xu, Z., Cheng, D., & Feng, Z. (2023). A data-driven approach to finding K for K nearest neighbor matching in average causal effect estimation. In F. Zhang (Ed.), Event/exhibition information: Web Information Systems Engineering – WISE 2023, Melbourne, 25/10/2023-27/10/2023
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2023 Tabassum, F., Mubarak, S., Liu, L., & Du, J. T. (2023). How many features do we need to identify bots on Twitter?. In Event/exhibition information: 18th International Conference, iConference 2023, Virtual, onine, 13/03/2023-17/03/2023
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2022 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). Recommending personalized interventions to increase employability of disabled jobseekers. In J. Gama (Ed.), Event/exhibition information: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, 16/05/2022-19/05/2022
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2020 Zhang, J., Nguyen, T., Truong, B., Liu, L., Li, J., & Le, T. D. (2020). Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data. In X. Yang, C. D. Wang, M. S. Islam, & Z. Zhang (Eds.), Event/exhibition information: 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, Foshan, China, 12/11/2020-14/11/2020
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2019 Le, T. D., Ong, K. L., Zhao, Y., Jin, W. H., Wong, S., Liu, L., & Williams, G. (2019). Preface - Data mining: AusDM: Australasian Conference on Data Mining. In Event/exhibition information: 17th Australasian Conference on Data Mining, AusDM 2019, Adelaide, Australia, 02/12/2019-05/12/2019
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2014 Liu, L., & Li, J. (2014). Building Naïve Bayes Classifiers with high-dimensional and small-sized datasets. In A. Agah (Ed.), Source details - Title: Medical applications of artificial intelligence (pp. 115-135). Boca Raton, Florida: CRC Press.
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2009 Liu, L., & Billington, J. (2009). Recursive parametric automata and ε-removal. In L. Lee, & D. David (Eds.), Event/exhibition information: 11th International Conference on Formal Methods for Open Object-Based Distributed Systems, Lisbon, Portugal, 09/06/2009-11/06/2009
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2025 Du, X., Li, J., Cheng, D., Liu, L., Gao, W., Chen, X., & Xu, Z. (2025). Telling Peer Direct Effects from Indirect Effects in Observational Network Data. In A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, . . . J. Zhu (Eds.), INTERNATIONAL CONFERENCE ON MACHINE LEARNING Vol. 267 (pp. 14562-14578). CANADA, Vancouver: JMLR-JOURNAL MACHINE LEARNING RESEARCH.
2025 Gao, W., Li, J., Liu, L., Le, T. D., Chen, X., Du, X., . . . Chen, Y. (2025). From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction. In I. Lynce, N. Murano, M. Vallati, S. Villata, F. Chesani, M. Milano, . . . M. Dastani (Eds.), In published processings need to add Proceedings of the 28th European Conference on Artificial Intelligence, including 14th Conference on Prestigious Applications of Intelligent Systems, as published in Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 1107-1114). Netherlands: IOS Press.
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2025 Ma'sum, M. A., Pratama, M., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). FEDERATED FEW-SHOT CLASS-INCREMENTAL LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 101524-101553).
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2025 Ma'Sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). VISION AND LANGUAGE SYNERGY FOR REHEARSAL FREE CONTINUAL LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 15997-16027).
2025 Du, X., Li, J., Cheng, D., Liu, L., Gao, W., Chen, X., & Xu, Z. (2025). Telling Peer Direct Effects from Indirect Effects in Observational Network Data. In Proceedings of the 42nd International Conference on Machine Learning, PMLR 267, 2025. Vol. 267 (pp. 1-17). US: ICML.
2025 Huang, Z., Cheng, D., Liu, L., Li, J., Lu, G., & Zhang, S. (2025). Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 2955-2963). Canada: International Joint Conferences on Artificial Intelligence Organization.
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2025 Masum, M. A., Pratama, D., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). PROL: Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning. In IEEE/CVF International Conference on Computer Visions (ICCV 2025) (pp. 1-11). US: IEEE/CVF.
2025 Ma'sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). Vision and language synergy for rehearsal free continual learning. In The Thirteenth International Conference on Learning Representations (ICLR 2025) (pp. 1-31). US: ICLR.
2025 Deng, J., Chen, Q., Cheng, D., Du, X., Li, J., & Liu, L. (2025). Mitigating Latent Confounding Bias in Recommender Systems. In Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 531-541). SOUTH KOREA: ASSOC COMPUTING MACHINERY.
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2025 Ma'sum, M. A., Pratama, M., Liu, L., Habibullah, H., & Kowalczyk, R. (2025). Federated few-shot class-incremental learning. In The Thirteenth International Conference on Learning Representations (ICLR 2025) (pp. 1-30). US: ICLR.
2025 Wang, H., Liu, L., Li, J., Xu, Z., Liu, J., Cao, Z., & Cheng, D. (2025). Off-policy Evaluation for Multiple Actions in the Presence of Unobserved Confounders. In Www 2025 Proceedings of the ACM Web Conference (pp. 413-424). US: ACM.
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2025 Chen, X., Li, J., Liu, J., Liu, L., Peters, S., Le, T. D., . . . Walsh, A. (2025). Diffusion Models for Attribution. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 39 (pp. 2266-2274). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
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2025 Gao, W., Li, J., Cheng, D., Liu, L., Liu, J., Le, T., . . . Zhao, Y. (2025). Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction. In Ijcai International Joint Conference on Artificial Intelligence (pp. 9638-9646). Canada: International Joint Conferences on Artificial Intelligence Organization.
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2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2024). Conditional instrumental variable regression with representation learning for causal inference. In 12th International Conference on Learning Representations, ICLR 2024 (pp. 1-17). US: International Conference on Learning Representations (ICLR).
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2024 Dzakpasu, D. Q., Liu, J., Li, J., & Liu, L. (2024). Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group Fairness. In International Conference on Information and Knowledge Management Proceedings (pp. 560-569). US: ACM.
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2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., Gao, W., & Le, T. D. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 38 (pp. 11480-11488). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
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2024 Chen, Q., Wei, B., Cheng, D., Li, J., Liu, L., & Zhang, S. (2024). Novel shadow variable catcher for addressing selection bias in recommendation systems. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 71-80). US: IEEE.
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2024 Ma'sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., & Kowalczyk, R. (2024). PIP: prototypes-injected prompt for Federated Class Incremental Learning. In CIKM ’24 Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 1670-1679). US: Association for Computing Machinery.
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2024 Xu, Z., Cheng, D., Li, J., Liu, J., Liu, L., & Yu, K. (2024). CAUSAL INFERENCE WITH CONDITIONAL FRONT-DOOR ADJUSTMENT AND IDENTIFIABLE VARIATIONAL AUTOENCODER. In 12th International Conference on Learning Representations Iclr 2024 (pp. 1-22). US: International Conference on Learning Representations (ICLR).
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2024 Guo, X., Yu, K., Liu, L., & Li, J. (2024). FedCSL: a scalable and accurate approach to federated causal structure learning. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 12235-12243). US: AAAI Press.
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2023 Xu, Z., Cheng, D., Li, J., Liu, J., Liu, L., & Wang, K. (2023). Disentangled Representation for Causal Mediation Analysis. In Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023 Vol. 37 (pp. 10666-10674). US: Association for the Advancement of Artificial Intelligence.
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2023 Cheng, D., Xie, Y., Xu, Z., Li, J., Liu, L., Liu, J., . . . Feng, Z. (2023). Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference. In Proceedings IEEE International Conference on Data Mining Icdm (pp. 51-60). US: IEEE.
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2023 Deho, O. B., Joksimovic, S., Liu, L., Li, J., Zhan, C., & Liu, J. (2023). Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution. In Proceedings of the 10th ACM Conference on Learning @ Scale (L@S 2023) (pp. 48-58). New York, NY, USA: Association for Computing Machinery (ACM).
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2023 Chen, X., Li, J., Liu, J., Peters, S., Liu, L., Le, T. D., & Walsh, A. (2023). Improve interpretability of Information Bottlenecks for Attribution with Layer-wise Relevance Propagation. In Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023 (pp. 1064-1069). US: IEEE.
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2023 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2023). Causal Inference with Conditional Instruments Using Deep Generative Models. In B. Williams, Y. Chen, & J. Neville (Eds.), Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023 Vol. 37 (pp. 7122-7130). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI Scopus21 WoS16
2023 Tran, H. X., Le, T. D., Li, J., Liu, L., Li, X., Liu, J., & Waters, T. (2023). Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4970-4980). US: ASSOC COMPUTING MACHINERY.
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2023 Cheng, D., Xu, Z., Li, J., Liu, L., Le, T. D., & Liu, J. (2023). Learning Conditional Instrumental Variable Representation for Causal Effect Estimation. In D. Koutra, C. Plant, M. G. Rodriguez, E. Baralis, & F. Bonchi (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 14169 LNAI (pp. 525-540). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus7 WoS6
2022 Le, T. D., Liu, L., Kiciman, E., Triantafyllou, S., & Liu, H. (2022). The KDD 2022 Workshop on Causal Discovery (CD2022). In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4884-4885). US: ASSOC COMPUTING MACHINERY.
DOI
2022 Cheng, D., Li, J., Liu, L., Zhang, J., Le, T. D., & Liu, J. (2022). Ancestral instrument method for causal inference without complete knowledge. In L. Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 4843-4849). US: International Joint Conferences on Artificial Intelligence Organization.
DOI Scopus8 WoS3
2022 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). What is the most effective intervention to increase job retention for this disabled worker?. In KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3981-3991). US: ACM - Association for Computing Machinery Inc.
DOI Scopus4 WoS2
2022 Xu, Z., Xu, Z., Liu, J., Cheng, D., Li, J., Liu, L., & Wang, K. (2022). Assessing Classifier Fairness with Collider Bias. In J. Gama, T. Li, Y. Yu, E. Chen, Y. Zheng, & F. Teng (Eds.), Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2022: Advances in Knowledge Discovery and Data Mining Vol. 13281 LNAI (pp. 262-276). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus4 WoS5
2022 Le, T. D., Liu, L., Kıcıman, E., Triantafyllou, S., & Liu, H. (2022). Preface: the 2022 ACM SIGKDD workshop on causal discovery. In Proceedings of Machine Learning Research Vol. 185 (pp. 1-2). US: ML Research Press.
2022 Xuan Tran, H., Duy Le, T., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). Decision Support for Disability Employment using Counterfactual Survival Analysis. In Proceedings 2022 IEEE International Conference on Big Data Big Data 2022 (pp. 2103-2112). US: IEEE.
DOI Scopus1
2022 Park, J. Y., Liu, L., Liu, J., & Li, J. (2022). Randomize Adversarial Defense in a Light Way. In Proceedings 2022 IEEE International Conference on Big Data Big Data 2022 (pp. 1080-1089). US: IEEE.
DOI Scopus3
2022 Deho, O. B., Liu, L., Joksimovic, S., Li, J., Zhan, C., & Liu, J. (2022). Assessing the Causal Impact of Online Instruction due to COVID-19 on Students' Grades and its aftermath on Grade Prediction Models. In ACM International Conference Proceeding Series (pp. 32-38). US: Association for Computing Machinery.
DOI Scopus5 WoS3
2021 Park, J. Y., Liu, L., Li, J., & Liu, J. (2021). Training Neural Networks with Random Noise Images for Adversarial Robustness. In International Conference on Information and Knowledge Management Proceedings (pp. 3358-3362). US: ASSOC COMPUTING MACHINERY.
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2021 Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2021). Divide and conquer: targeted adversary detection using proximity and dependency. In Z. Gong, X. Li, S. G. Oguducu, L. Chen, B. F. Manjon, & X. Wu (Eds.), Proceedings - 12th IEEE International Conference on Big Knowledge, ICBK 2021 (pp. 125-132). US: IEEE.
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2021 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2021). Recommending the most effective intervention to improve employment for job seekers with disability. In KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3616-3626). US: Association for Computing Machinery.
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2021 Zhang, W., Liu, L., & Li, J. (2021). Treatment effect estimation with disentangled latent factors. In Proceedings from the 35th AAAI conference of artificial intelligence Vol. 12B (pp. 10923-10930). US: Association for the Advancement of Artificial Intelligence.
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2020 Zhang, W., Liu, L., & Li, J. (2020). Robust multi-instance learning with stable instances. In G. Giacomo (Ed.), ECAI 2020: 24th European Conference on Artificial Intelligence, 29 August–8 September 2020, Santiago de Compostela, Spain, Proceedings Vol. 325 (pp. 1682-1689). Netherlands: IOS Press.
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2020 Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2020). LoPAD: A local prediction approach to anomaly detection. In H. W. Lauw, R. C. W. Wong, A. Ntoulas, E. P. Lim, S. K. Ng, & S. J. Pan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12085 (pp. 660-673). Singapore: Springer.
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2020 Xuan Tran, H., Duy Le, T., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2020). Intervention Recommendation for Improving Disability Employment. In X. T. Wu, C. Jermaine, L. Xiong, X. H. Hu, O. Kotevska, S. Y. Lu, . . . J. Saltz (Eds.), Proceedings 2020 IEEE International Conference on Big Data Big Data 2020 (pp. 1671-1680). US: IEEE.
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2020 Cheng, D., Li, J., Liu, L., Liu, J., Yu, K., & Le, T. D. (2020). Causal query in observational data with hidden variables. In G. DeGiacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Eds.), Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 2551-2558). Netherlands: IOS PRESS.
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2020 Le, T. D., Liu, L., Zhang, K., Kıcıman, E., Cui, P., & Hyvarinen, A. (2020). Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. In Proceedings of Machine Learning Research Vol. 127 (pp. 1-3). US: ML Research Press.
2020 Islam, M. Z., Liu, J., Li, J., Liu, L., & Kang, W. (2020). Evidence Weighted Tree Ensembles for Text Classification. In SIGIR 2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1737-1740). US: ACM.
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2020 Liu, J., Li, J., Ye, F., Liu, L., Le, T., Xiong, P., & Liu, H. C. (2020). Building fair predictive models. In M. Gallagher, N. Moustafa, & E. Lakshika (Eds.), 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020 Proceedings Vol. 12576 LNAI (pp. 216-229). Switzerland: Springer Nature.
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2019 Lu, S., Liu, L., Li, J., & Le, T. D. (2019). Effective outlier detection based on Bayesian Network and Proximity. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, . . . J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 134-139). US: IEEE.
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2019 Islam, M. Z., Liu, J., Li, J., Liu, L., & Kang, W. (2019). A semantics aware random forest for text classification. In International Conference on Information and Knowledge Management Proceedings (pp. 1061-1070). China: ASSOC COMPUTING MACHINERY.
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2019 Ansah, J., Kwashie, S., Liu, L., Liu, J., Kang, W., & Li, J. (2019). A graph is worth a thousand words: Telling event stories using timeline summarization graphs. In Web Conference 2019 Proceedings of the World Wide Web Conference Www 2019 (pp. 2565-2571). US: ACM Digital Library.
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2019 Liu, J., Kwashie, S., Li, J., Liu, L., & Bewong, M. (2019). Linking graph entities with multiplicity and provenance. In Ceur Workshop Proceedings Vol. 2446 (pp. 1-7). Germany: Rheinisch-Westfaelische Technische Hochschule Aachen Lehrstuhl Informatik V.
2019 Le, T. D., Li, J., Zhang, K., Kıcıman, E., Cui, P., & Hyvärinen, A. (2019). Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. In Proceedings of Machine Learning Research Vol. 104 (pp. 1-3). US: ML Research Press.
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2019 Islam, M. Z., Liu, J., Liu, L., Li, J., & Kang, W. (2019). Semantic explanations in ensemble learning. In Q. Yang, Z. H. Zhou, Z. Gong, M. L. Zhang, & S. J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14–17, 2019, Proceedings, Part I Vol. 11439 LNAI (pp. 29-41). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus5 WoS3
2018 Ansah, J., Kang, W., Liu, L., Liu, J., & Li, J. (2018). SensorTree: Bursty Propagation Trees as Sensors for Protest Event Detection. In H. Hacid, W. Cellary, H. Wang, H. Y. Paik, & R. Zhou (Eds.), Web Information Systems Engineering – WISE 2018 19th International Conference Vol. 11233 LNCS (pp. 281-296). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus6 WoS5
2018 Le, T. D., Zhang, K., Kıcıman, E., Hyvarinen, A., & Liu, L. (2018). Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. In Proceedings of Machine Learning Research Vol. 92 (pp. 1-3). US: ML Research Press.
2018 Ansah, J., Kang, W., Liu, L., Liu, J., & Li, J. (2018). Information propagation trees for protest event prediction. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10939 LNAI (pp. 777-789). Switzerland: Springer International Publishing.
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2018 Le, T. D., Xu, T., Liu, L., Shu, H., Hoang, T., & Li, J. (2018). ParallelPC: An R package for efficient causal exploration in genomic data. In M. Ganji, L. Rashidi, B. C. M. Fung, & C. Wang (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 11154 LNAI (pp. 207-218). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus9 WoS3
2018 Zhang, Z., Li, J., Wang, H., Liu, L., & Liu, J. (2018). Which type of classifier to use for networked data, connectivity based or feature based?. In H. Hacid (Ed.), Web Information Systems Engineering – WISE 2018 proceedings Vol. 11233 LNCS (pp. 364-380). Germany: Springer.
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2017 Liu, J., Liu, L., & Chen, T. (2017). Evaluating and improving SIP non-INVITE transaction to alleviate the losing race problem. In W. Aalst, & E. Best (Eds.), Application and Theory of Petri Nets and Concurrency Vol. 10258 LNCS (pp. 57-77). Switzerland: Springer.
DOI Scopus2 WoS2
2017 Li, J., Liu, J., Liu, L., Le, T. D., Ma, S., & Han, Y. (2017). Discrimination detection by causal effect estimation. In J. Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, . . . M. Toyoda (Eds.), Proceedings 2017 IEEE International Conference on Big Data Big Data 2017 Vol. 2018-January (pp. 1087-1094). US: IEEE.
DOI Scopus12 WoS11
2017 Le, T. D., Zhang, J., Liu, L., Thanh Truong, B. M., Hu, S., Xu, T., & Li, J. (2017). Identifying microRNA targets in epithelial-mesenchymal transition using joint-intervention causal inference. In ACM International Conference Proceeding Series (pp. 34-41). US: ASSOC COMPUTING MACHINERY.
DOI Scopus2 WoS1
2017 Bewong, M., Liu, J., Liu, L., Li, J., & Choo, K. K. R. (2017). A relative privacy model for effective privacy preservation in transactional data. In Proceedings 16th IEEE International Conference on Trust Security and Privacy in Computing and Communications 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems Trustcom Bigdatase Icess 2017 (pp. 394-401). US: IEEE.
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2017 Bewong, M., Liu, J., Liu, L., & Li, J. (2017). Utility aware clustering for publishing transactional data. In J. Kim, K. Shim, L. Cao, J. G. Lee, X. Lin, & Y. S. Moon (Eds.), Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23–26, 2017, Proceedings, Part II Vol. 10235 LNAI (pp. 481-494). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus8 WoS9
2017 Kang, W., Chen, J., Li, J., Liu, J., Liu, L., Osborne, G., . . . Neale, G. (2017). Carbon: Forecasting civil unrest events by monitoring news and social media. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 10604 LNAI (pp. 859-865). Singapore: Springer.
DOI Scopus11 WoS8
2016 Chen, J., Kang, W., Li, J., Liu, J., Liu, L., Cooper, B., . . . Moschou, T. (2016). A temporal classification based predictive model of recurring societal events. In 14th Australasian Data Mining Conference Ausdm 2016 (pp. 93-99). Australia: Australian Computer Society.
2016 Le, T. D., Hoang, T., Li, J., Liu, L., & Liu, H. (2016). A fast PC algorithm for high dimensional causal discovery for multi-core PCs. In International workshop on data mining in bioinformatics (pp. 1-10). US: IEEE.
2016 Azfar, A., Choo, K. K. R., & Liu, L. (2016). An android social app forensics adversary model. In Proceedings of the Annual Hawaii International Conference on System Sciences Vol. 2016 (pp. 5597-5606). US: IEEE.
DOI Scopus35 WoS25
2015 Azfar, A., Choo, K. K. R., & Liu, L. (2015). Forensic taxonomy of popular Android mHealth apps. In AMCIS 2015 Proceedings: Healthcare Information Systems and Technology (SIGHealth) (pp. 1-19). US: Association for Information Systems.
Scopus29 WoS4
2014 Liu, J., & Liu, L. (2014). A coloured petri net approach to the functional and performance analysis of SIP non-INVITE transaction. In M. Koutny, S. Haddad, & A. Yakovlev (Eds.), Transactions on Petri Nets and Other Models of Concurrency IX Vol. 8910 (pp. 147-177). Germany: Springer.
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2014 Karim, M. S. M., Liu, L., & Li, J. (2014). Discovering collective group relationships. In E. Burke, & M. Trick (Eds.), Databases Theory and Applications: 25th Australasian Database Conference, ADC 2014, Brisbane, QLD, Australia, July 14-16, 2014. Proceedings Vol. 8506 LNCS (pp. 110-121). Switzerland: Springer.
DOI Scopus2 WoS2
2014 Azfar, A., Choo, K. K. R., & Liu, L. (2014). A study of ten popular Android mobile VoIP applications: are the communications encrypted?. In R. H. Sprague (Ed.), Proceedings of the 47th Annual Hawaii International Conference on System Sciences (pp. 4858-4867). US: IEEE.
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2013 Li, J., Le, T. D., Liu, L., Liu, J., Jin, Z., & Sun, B. (2013). Mining causal association rules. In W. Ding, T. Washio, H. Xiong, G. Karypis, B. Thuraisingham, D. Cook, & X. Wu (Eds.), IEEE 13th International Conference on Data Mining Workshops 2013 proceedings (pp. 114-123). US: IEEE Press.
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2013 Li, J., Zhang, K., Pei, J., Liu, L., Lu, Z. H., & Zhang, J. (2013). Preface to the first IEEE ICDM workshop on causal discovery. In Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 (pp. 1-2). US: IEEE.
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2012 Jin, Z., Li, J., Liu, L., Le, T. D., Sun, B., & Wang, R. (2012). Discovery of causal rules using partial association. In M. J. Zaki, A. Siebes, J. X. Yu, B. Goethals, G. Webb, & X. Wu (Eds.), Proceedings IEEE International Conference on Data Mining Icdm (pp. 309-318). US: IEEE.
DOI Scopus30 WoS24
2011 Zhang, Z., Liu, L., Li, J., & Zhang, Z. (2011). Spectral representation of DNA sequences and its application. In Proceedings 2010 IEEE fifth international conference on bio-inspired computing : theories and applications (pp. 1023-1027). US: IEEE.
DOI Scopus6
2011 Wang, Y., & Liu, L. (2011). Controller design using coloured Petri nets: with a case study of the papermaking process control. In Proceedings of the 2011 International Conference on Modelling, Identification and Control (pp. 415-421). Shanghai: IEEE.
DOI Scopus2
2011 Liu, L. (2011). Uncovering SIP vulnerabilities to DoS attacks using coloured Petri nets. In Proceedings of the 10th IEEE International conference on trust, security and privacy in computing and communications (pp. 29-36). US: IEEE.
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2010 Liu, L., Li, Y., Liu, B., & Li, J. (2010). A simple yet effective data integration approach to tree-based microarray data classification. In 32nd annual international conference of the IEEE engineering in medicine and biology society Vol. 2010 (pp. 1503-1506). US: IEEE.
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2010 Liu, L. (2010). Security analysis of session initiation protocol : a methodology based on coloured petri nets. In V. Valli, & C. Craig (Eds.), Proceedings of the 1st International Cyber Resilience Conference. Perth, Western Australia: Security Research Centre, School of Computer and Security Science, Edith Cowan University.
2009 Liu, L. (2009). Verification of the SIP transaction using coloured petri nets. In M. Mans, & B. Bernard (Eds.), Proceedings of the 32nd Australasian Computer Science Conference (ACSC 2009) Vol. 91 (pp. 75-84). Sydney, Australia: Austrailan Computer Society.
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2008 Ding, L. G., & Liu, L. (2008). Modelling and analysis of the INVITE transaction of the session initiation protocol using coloured petri nets. In Proceedings of the 29th International conference on applications and theory of petri nets, PETRI NETS 2008. Applications and theory of petri nets, LNCS series 5062 Vol. 5062 LNCS (pp. 132-151). Berlin: Springer.
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2007 Liu, L., & Billington, J. (2007). Symbolic language representations for parametric verification of the revised capability exchange signalling protocol. In M. Munro, & D. D (Eds.), Proceedings of the Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies (pp. 480-487). USA: IEEE Computer Society.
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2007 Liu, L., & Billington, J. (2007). Symbolic language representations for parametric verification of the revised capability exchange signalling protocol. In Parallel and Distributed Computing Applications and Technologies PDCAT Proceedings (pp. 480-487). IEEE.
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2005 Liu, L., & Billington, J. (2005). Enhancing the CES protocol and its verification. In Proceedings of the Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools. Denmark: University of Aarhus.
2004 Liu, L., Liu, L., & Billington, J. (2004). Obtaining the service language for H.245's multimedia capability exchange signalling protocol: the final step. In Y. P. P. Chen (Ed.), 10th international multimedia modelling conference (MMM 2004) (pp. 323-328). Los Alamitos, Calif. USA: IEEE Computer Society.
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2004 Liu, L., & Billington, J. (2004). Reducing parametric automata : a multimedia protocol service case study. In Automated Technology for Verification and Analysis Vol. 3299 (pp. 483-486). Berlin, Germany: Springer-Verlag.
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2004 Liu, L., & Billington, J. (2004). Reducing parametric automata: a multimedia protocol service case study. In Lecture Notes in Computer Science Vol. 3299 (pp. 483-486). Berlin, Germany: Springer-Verlag.
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2002 Liu, L., & Billington, J. (2002). Tackling the infinite state space of a multimedia control protocol service specification. In Lecture notes in computer science Vol. 2360 LNCS (pp. 273-293). Germany: Springer-Verlag.
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2001 Liu, L., & Billington, J. (2001). Modelling and analysis of the CES Protocol of H.245. In Proceedings: 3rd Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools. http://www.daimi.au.dk/CPnets/workshop01/cpnpapers/.
2001 Liu, L., & Billington, J. (2001). Modelling and analysis of internet multimedia protocols - methodology and initial results. In 11th Annual International Symposium of the International Council on Systems Engineering Vol. 11 (pp. 258-265). US: International Council of Systems Engineering.
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1999 Pan, H., & Liu, L. (1999). Fuzzy Bayesian networks - A general formalism for representation, inference and learning with hybrid Bayesian networks. In Iconip 1999 6th International Conference on Neural Information Processing Proceedings Vol. 1 (pp. 401-406). IEEE.
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1999 McMichael, D., Liu, L., & Pan, H. (1999). Estimating the parameters of mixed Bayesian networks from incomplete data. In Idc 1999 1999 Information Decision and Control Data and Information Fusion Symposium Signal Processing and Communications Symposium and Decision and Control Symposium Proceedings (pp. 591-596). IEEE.
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1993 Zhang, Y., & Lu, L. (1993). Knowledge-based data fusion strategies for airborne multisensor surveillance system. In Proceedings of the IEEE International Conference on Systems Man and Cybernetics Vol. 3 (pp. 646-649). IEEE.
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Year Citation
2026 Pinero, S., Li, X., Liu, L., Li, J., Lee, S. H., & Le, T. D. (2026). SPLIT: Safety Prioritization for Long COVID Drug Repurposing via a Causal Integrated Targeting Framework.
DOI
2025 Liu, X., Zhang, W., Tang, W., Le, T. D., Li, J., Liu, L., & Zhang, M. -L. (2025). From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification.
2025 Vu, T., Tran, H., Liu, L., Li, J., Du, J. T., & Le, T. (2025). Foundation Model-Based Recommendation of Optimal Neoadjuvant Therapy in Breast Cancer.
DOI
2025 Piñero, S., Li, X., Liu, L., Li, J., Lee, S. H., Winter, M., . . . Le, T. D. (2025). TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID.
DOI
2025 Vu, T., Tran, H., Li, X., Liu, L., Li, J., Du, J. T., & Le, T. (2025). Tabular Foundation Model for Breast Cancer Prognosis using Gene Expression Data.
DOI
2025 Pinero, S., Li, X., Zhang, J., Winter, M., Lee, S. H., Nguyen, T., . . . Le, T. D. (2025). Omics-Based Computational Approaches for Biomarker Identification, Prediction, and Treatment of Long COVID.
DOI
2025 Pinero, S., Li, X., Liu, L., Li, J., Lee, S. H., Winter, M., . . . Le, T. D. (2025). Integrative Multi-Omics Framework for Causal Gene Discovery in Long COVID.
DOI
2023 Cifuentes-Bernal, A. M., Liu, L., Li, J., & Le, T. D. (2023). Identifying cooperative genes causing cancer progression with dynamic causal inference.
DOI

  • Build competency aware and assuring machine learning systems, ARC - Discovery Projects, 01/01/2023 - 31/12/2026

  • On-orbit evaluation and demonstration of energy-efficient fire smoke detection on the Kanyini and the Phi-Sat-2 CubeSat during 2025 and 2026 fire season using HS2 imagery and onboard AI., SmartSat CRC, 18/12/2024 - 18/04/2026

  • ARC ITTC in Cognitive Computing for Medical Technologies, ARC - Industrial Transformation Training Centres, 13/12/2017 - 31/12/2024

  • Develop efficient data mining methods for evidence based decision making, ARC - Discovery Projects, 29/06/2017 - 28/06/2021

  • Int Policing: Entity Linking & Resolution, D2D CRC Limited, 01/01/2016 - 31/12/2019

  • D2D CRC Limited Scholarship, D2D CRC Limited, 16/03/2016 - 30/09/2019

  • Efficient causal discovery from observational data, ARC - Discovery Projects, 19/06/2014 - 31/12/2017

Courses I teach

  • INFS 5102 Unsupervised Methods in Analytics (2025)
  • INFS 5102 Unsupervised Methods in Analytics (2024)

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Co-Supervisor Enhancing students learning experience through personalised assessments Doctor of Philosophy Doctorate Full Time Linqing Li
2025 Principal Supervisor Developing Causal Unlearning Techniques for Responsible AI Doctor of Philosophy Doctorate Full Time Yi Li
2025 Co-Supervisor Physics-informed Self-supervised Learning for Optical Satellite-based Wildfire Detection with Onboard Distillation - Doctorate Full Time Kaylen Smith
2025 Co-Supervisor Physics-informed Self-supervised Learning for Optical Satellite-based Wildfire Detection with Onboard Distillation Doctor of Philosophy Doctorate Full Time Kaylen Smith
2024 Co-Supervisor Work-integrated learning for international students: Prepare them for working life in the global job market Doctor of Philosophy Doctorate Full Time Mrs Jin Cui
2023 Co-Supervisor Developing Effective Algorithms for Estimating Causal Effects in Observational Network Data Doctor of Philosophy Doctorate Full Time Miss Xiaojing Du
2023 Co-Supervisor 109492- Developing causal-based methods for recommending the repurposed drugs for a disease and applications in breast cancer and SARS-CoV-2 Doctor of Philosophy Doctorate Full Time Mr Tuyen Vu
2023 Principal Supervisor 110459 - Advancing machine learning for human-machine partnership in decision making Doctor of Philosophy Doctorate Full Time Mr Haolin Wang
2023 Principal Supervisor Transformer-based causal inference methods for temporal data with latent confounders Doctor of Philosophy Doctorate Full Time Mr Xudong Guo
2023 Co-Supervisor Develop causality-based models for robust and adaptive drought predictions Doctor of Philosophy Doctorate Full Time Wentao Gao
2022 Co-Supervisor span class="cf0">Advanced time series domain adaptation Doctor of Philosophy Doctorate Full Time Muhammad Tanzil Furqon
2022 Co-Supervisor Computational methods for finding causal biomarkers and treatments for COVID-19 - Doctorate Full Time Ms Sindy Pinero
2022 Co-Supervisor Develop causal outcome prediction models for trustworthy interpretation Doctor of Philosophy Doctorate Full Time Xiongren Chen
2022 Co-Supervisor Computational methods for finding causal biomarkers and treatments for COVID-19 Doctor of Philosophy Doctorate Full Time Ms Sindy Pinero

Date Title Engagement Type Institution Country
2019 - ongoing The Australasian Data Science and Machine Learning Conference series (AusDM) Scientific Community Engagement AusDM Australia

Date Role Editorial Board Name Institution Country
2024 - ongoing Associate Editor IEEE Transactions on Artificial Intelligence IEEE Computational Intelligence Society United States
2020 - ongoing Associate Editor BMC Bioinformatics BMC United Kingdom

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