Jiuyong Li

Research Interests

Artificial Intelligence Bioinformatics Data and information privacy Data mining and knowledge discovery Machine learning

Prof Jiuyong Li

Professor of Computer Science

School of Computer Science and Information Technology

College of Engineering and Information Technology

Alias: John Li OR "Jui yon Lee" for my name Jiuyong Li.  
Jiuyong Li is a Professor of Computer Science at STEM of the University of South Australia (UniSA). He is the Director of the Data Analytics Group. His research interests are in data mining, applied machine learning and their applications in bio and health informatics and for social good, such as privacy and fairness. His research work has been supported by seven Australian Research Council Discovery grants and many industry funds. He is a member of the National Committee of Artificial Intelligence of Australian Computer Society.
My paper lists: Google Scholar and DBLP (Computer Science) 
Software tools: http://4llab.net/sw.html
More informaiton about the Data Analytics Group: http://4llab.net/index.html
Major research grants (selected)

ARC Discovery grant (2023 - 2025, DP230101122), Lin Liu, Jiuyong Li, Jixue Liu and Thuc Le, Build competency aware and assuring machine learning systems.
SmartSat CRC project (2022 - 2023), Stefan Peters, Jiuyong Li, Jixue Liu and Kai Qin. Energy-efficient on-board AI for early fire smoke detection.
iMove CRC project (2021 - 2023), Akshay Marian Vij, Marin Romeo and Jiuyong Li. Big data for strategic transport planning.
ARC Discovery grant (2021 - 2023, DP200101210), Jiuyong Li, Jixue Liu and Ke Wang, Fairness aware data mining methods for discrimination free decision-making.
ARC Discovery grant (2017 - 2019, DP170101306), Jiuyong Li, Lin Liu, Peng Cui, and Gregory Goodall, Efficient data mining methods for evidence-based decision making.
CRC Data to Decision project (2015 - 2018), Jiuyong Li, et al. Beat the news - building classification and prediction systems for civil unrest events.
ARC Discovery grant (2014 - 2016, DP140103617), Jiuyong Li, Lin Liu, Zeng-Hua Lu, and Gregory Goodall, Efficient causal discovery from observational data.
ARC Discovery grant (2013 - 2015, DP130104090), Jiuyong Li, Lin Liu, Jian Pei, and Gregory Goodall, Developing novel data mining methods to reveal complex group relationships from heterogeneous data.
ARC Discovery grant (2011 - 2013, DP110103142), Jiuyong Li and Bradley Malin, Studying privacy protection methods for multiple independent data releases
ARC Discovery grant (2007 - 2009, DP0774450), Jiuyong Li and Hua Wang, Privacy preserving data sharing in data mining environment
ARC Discovery grant (2005 - 2007, DP0559090), Jiuyong Li, Investigation and development of robust rule discovery and classification system

Major community service (selected)

Member of the Australian Computer Society National Committee for Artificial Intelligence, 2011 - 2021.
Member of the Steering committee, Australasian Data Mining Conference (AusDM), since 2007.
Member the AI4Space Steering Committee of SmartSAt CRC, since 2020.
Associate Editor, International Journal of Data Science and Analytics, since 2018.
Co-guest editor ACM Transactions on Intelligent Systems and Technology special issue on Advances of Causal Discovery and Inference, 2018.
Co-guest editor ACM Transactions on Intelligent Systems and Technology special issue on Causal Discovery and Inference, 2015.
Co-guest editor Springer Journal of Data Science and Analytics special issue on Causal Discovery, 2016, 2017.
Co-chair ACM KDD Workshop on Causal Discovery (CD), 2016, 2017, 2019, 2021.
Co-chair, The 7th IEEE International Conference on Smart Data, 2021.
Co-chair Australasian Joint Conference on Artificial Intelligence, 2019.
Co-chair, ICDM workshop: International Workshop on Social Computing (IWSC’18): Spatial Social Behavior Analytics in Urban Society, 2018.
Co-chair International Workshop on Machine Learning for Sensory Data Analysis (MLSDA), 2014, 2016.
PC co-chair, IFIP International Conference on Intelligent Information Processing, 2018.
Area chair, Conference on Uncertainty in Artificial Intelligence UAI 2022, 2023.
Area chair, IEEE International Conference on Data Mining, 2014, 2015, 2021.
Area Chair, International Joint Conference on Artificial Intelligence (IJCAI), 2021.
Area chair, The 11th IEEE International Conference on Knowledge Graph, 2021.
Area Chair, IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2021.
Area Chair, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023.
Senior PC, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021, 2022.
Senior PC AAAI Conference on Artificial Intelligence (AAAI) 2019, 2022.
Senior PC International Joint Conference on Artificial Intelligence (IJCAI), 2017, 2020, 2022.
Senior PC Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021. 2022, 2023.
Senior PC ACM Conference on Information and Knowledge Management (CIKM), 2019.

  • Data mining / Machine Learning
  • Biomedical informatics
  • Fairnee aware computing

Date Position Institution name
2022 - 2007 Lecturer / Senior Lecturer University of Southern Queensland
2013 - 2020 Associate Head of School (Research) University of South Australia
2012 - 2025 Professor University of South Australia
2010 - 2012 Research Education Portfolio Leader University of South Australia
2007 - 2011 Associate Professor University of South Australia

Date Institution name Country Title
1999 - 2001 Griffith University Australia PhD
1995 - 1998 Yunnan University China MPhil

Year Citation
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
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 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 Scopus1
2026 Cheng, D., Xu, Z., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2026). Linking model intervention to causal interpretation in model explanation. Pattern Recognition, 173, 112814.
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 Scopus1
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
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 Scopus8
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 Scopus15 WoS6
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 Scopus4 WoS2
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
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 Scopus1 WoS1
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
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.
DOI
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 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
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
2024 Zhao, L., Liu, J., Peters, S., Li, J., Mueller, N., & Oliver, S. (2024). Cross-sensor transfer learning for fire smoke scene detection using variable-bands multi-spectral satellite imagery aided by spectral patterns. International Journal of Remote Sensing, 45(10), 3332-3348.
DOI
2024 Shi, Z., Chow, C. W. K., Gao, J., Xing, K., Liu, J., & Li, J. (2024). Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations. Sensors, 24(6), 1857.
DOI Scopus4 WoS3 Europe PMC1
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 Scopus8
2024 Liu, J., Li, J., Peters, S., & Zhao, L. (2024). A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery. Remote Sensing Applications Society and Environment, 36(101283), 101283.
DOI Scopus3
2024 Zhao, L., Liu, J., Peters, S., Li, J., Mueller, N., & Oliver, S. (2024). Learning class-specific spectral patterns to improve deep learning-based scene-level fire smoke detection from multi-spectral satellite imagery. Remote Sensing Applications Society and Environment, 34(101152), 101152.
DOI Scopus11
2024 Lu, S., Jones, E., Zhao, L., Sun, Y., Qin, K., Liu, J., . . . Peters, S. (2024). Onboard AI for Fire Smoke Detection Using Hyperspectral Imagery: An Emulation for the Upcoming Kanyini Hyperscout-2 Mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 9629-9640.
DOI Scopus8 WoS7
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 Scopus3 WoS3 Europe PMC3
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 Scopus9 WoS8 Europe PMC2
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 WoS2
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 Scopus5 WoS2
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 Scopus32 WoS22
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 Scopus31 WoS23 Europe PMC1
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 Scopus6
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 Scopus8 WoS2
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 Scopus16
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 Scopus17 WoS12
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 Scopus25
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 Scopus24 WoS13
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 Scopus16 WoS11
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 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 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 WoS13
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 Scopus23 WoS25 Europe PMC3
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 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 Scopus30 Europe PMC2
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 Scopus34 WoS30
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
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
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 Scopus28
2022 Chow, C. W. K., Liu, J., Li, J., Swain, N., & Saint, C. P. (2022). A Data Visualisation Tool for Treatment Process Monitoring in Web Browsers. Water Conservation Science and Engineering, 7(4), 363-373.
DOI Scopus2
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 Scopus48
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 WoS3 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 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 Scopus58 WoS40
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 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 Scopus18 WoS17 Europe PMC18
2022 Zhao, L., Liu, J., Peters, S., Li, J., Oliver, S., & Mueller, N. (2022). Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model. Remote Sensing, 14(13), 24 pages.
DOI Scopus38 WoS28
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 Scopus5 WoS5 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 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 WoS3 Europe PMC3
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 Scopus16 WoS14 Europe PMC16
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 WoS21 Europe PMC13
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 PMC10
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 Scopus82 WoS72
2021 Peters, S., Liu, J., Bruce, D., Li, J., Finn, A., & O’Hehir, J. (2021). Research note: cost-efficient estimates of Pinus radiata wood volumes using multitemporal LiDAR data. Australian Forestry, 84(4), 206-214.
DOI Scopus4 WoS4
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 Scopus24 WoS21 Europe PMC3
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 Scopus170 WoS151
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 PMC20
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 Scopus6 WoS7 Europe PMC4
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 Scopus14 WoS12 Europe PMC9
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 Scopus11 WoS9 Europe PMC9
2020 Truong, B., Zhou, X., Shin, J., Li, J., van der Werf, J. H. J., Le, T. D., & Lee, S. H. (2020). Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives. Nature Communications, 11(1), 11 pages.
DOI Scopus23 WoS19 Europe PMC21
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 PMC22
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 Scopus38 WoS38
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 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 Scopus112 WoS92 Europe PMC8
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 Scopus20 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 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 Scopus34 WoS27 Europe PMC29
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 PMC15
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 Scopus206 WoS199 Europe PMC212
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
2019 Brown, P., Tan, A. -C., El-Esawi, M. A., Liehr, T., Blanck, O., Gladue, D. P., . . . Zhou, Y. (2019). Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database: the journal of biological databases and curation, 2019(baz085), baz085-1-baz085-66.
DOI Scopus28 WoS36 Europe PMC13
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.
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2013 Jiao, H., Liu, J., Li, J., & Liu, C. (2013). A two-layer multi-dimensional trustworthiness metric for web service composition. Web Technologies and Applications: 15th Asia-Pacific Web Conference, APWeb 2013, Sydney, Australia, April 2013. Proceedings, 7808 LNCS, 151-162.
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2012 Sun, X., Wang, H., Li, J., & Zhang, Y. (2012). An approximate microaggregation approach for microdata protection. Expert systems with applications, 39(2), 2211-2219.
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2012 Natarajan, K., Li, J., & Koronios, A. (2012). Detecting data inconsistencies by multiple target rules. International Journal of Business and Systems Research, 6(3), 296-312.
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Year Citation
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
Source details - Title: AI 2024: Advances in Artificial Intelligence (Vol. 15442 LNAI, pp. 139-153). Singapore: 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
Source details - Title: AI 2024: Advances in Artificial Intelligence (Vol. 15442 LNAI, pp. 291-302). Singapore: SPRINGER-VERLAG SINGAPORE PTE LTD.
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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
Source details - Title: Australasian Joint Conference on Artificial Intelligence AI 2023: AI 2023: Advances in Artificial Intelligence (Vol. 14471 LNAI, pp. 315-327). Singapore: Springer Nature Singapore.
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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
Source details - Title: Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2023: Advances in Knowledge Discovery and Data Mining (Vol. 13935 LNCS, pp. 471-482). Switzerland: Springer.
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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
Source details - Title: Web Information Systems Engineering –WISE 202324th International Conference Melbourne, VIC, Australia, October 25–27, 2023 Proceedings (Vol. 14306 LNCS, pp. 723-732). Singapore: Springer.
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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
Source details - Title: Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part III (Vol. 13282 LNAI, pp. 92-104). Switzerland: Springer.
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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 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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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 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. (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 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). ACM.
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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 Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 1107-1114). IOS Press.
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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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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 Zhang, G., Yuan, G., Cheng, D., He, L., Bing, R., Li, J., & Zhang, S. (2024). Multi-view graph neural network for fair representation learning. In W. Zhang (Ed.), Web and Big Data 8th International Joint Conference, APWeb-WAIM 2024 Jinhua, China, August 30 – September 1, 2024 Proceedings, Part III Vol. 14963 LNCS (pp. 208-223). Singapore: Springer.
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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 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 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 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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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 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.
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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 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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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.
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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.
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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.
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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.
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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.
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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.
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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 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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2021 Le, T. D., Li, J., Cooper, G., Triantafyllou, S., Bareinboim, E., Liu, H., & Kiyavash, N. (2021). The KDD 2021 Workshop on Causal Discovery (CD2021). In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4141-4142). US: ASSOC COMPUTING MACHINERY.
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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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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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2018 Carman, M., Koerber, M., Li, J., Choo, K. K. R., & Ashman, H. (2018). Manipulating visibility of political and apolitical threads on reddit via score boosting. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 (pp. 184-190). US: IEEE.
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2017 Li, J., Qiu, R., Zhao, Y., & Ma, H. (2017). Effect of heat treatment temperature on the frowth of Al / Ti joining interface reaction layer. In Proceedings Of The 2017 3rd International Forum On Energy, Environment Science And Materials (ifeesm 2017) Vol. 120 (pp. 847-850). US: Atlantis Press.
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2014 Nofong, V. M., Liu, J., & Li, J. (2014). A study on the applications of emerging sequential patterns. In H. Wang, & M. A. Sharaf (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8506 LNCS (pp. 62-73). US: SPRINGER-VERLAG BERLIN.
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2014 Kwashie, S., Liu, J., Li, J., & Ye, F. (2014). Mining differential dependencies: A subspace clustering approach. In H. Wang, & M. A. Sharaf (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 8506 LNCS (pp. 50-61). US: SPRINGER-VERLAG BERLIN.
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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.
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2013 Zhang, Q., Zhang, J., Gao, J., He, J., Yan, X., Ma, L., & Li, J. (2013). Motif discovery and phylogenetic analysis of hepatitis B virus sequences. In IFMBE proceedings Vol. 39 (pp. 1268-1271). Germany: Springer.
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2013 Xia, W., Heatherly, R., Ding, X., Li, J., & Malin, B. (2013). Efficient discovery of de-identification policy options through a risk-utility frontier. In Proceeding of ACM Conference on Data and Application Security and Privacy (CODASPY) Vol. 2013 (pp. 59-70). USA: ACM - Association for Computing Machinery Inc.
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2011 Jiao, H., Liu, J., & Li, J. (2011). Recognize trustworthy web services via institutions. In R. Zhang, J. Cordeiro, X. Li, Z. Zhang, & J. Zhang (Eds.), Iceis 2011 Proceedings of the 13th International Conference on Enterprise Information Systems Vol. 4 SAIC (pp. 187-190). China: INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION.
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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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2009 Lock, P., Le, M. N., Li, J., & Stumptner, M. (2009). Building a generic graph-based descriptor set for use in drug discovery. In K. Kennedy, & P. J (Eds.), Proceedings of the 8th Australasian data mining conference (AUsDM-09) Vol. 101 (pp. 167-174). Australia: Australian Computer Society.
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2009 Sun, X., Wang, H., & Li, J. (2009). Microdata protection through approximate microaggregation. In M. Mans, & B. Bernard (Eds.), Proceedings of the 32nd Australasian Conference on Computer Science, (ACSC09) Vol. 91 (pp. 161-168). Australia: Austrailan Computer Society.
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2009 Sun, X., Wang, H., Li, J., & Ross, D. (2009). Achieving p-sensitive k-anonymity via anatomy. In Proceedings : 2009 IEEE International Conference on e-Business Engineering (pp. 199-205). US: IEEE.
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2009 Baig, M. M., Li, J., Liu, J., & Wang, H. (2009). Studying genotype-phenotype attack on k-anonymised medical and genomic data. In Conferences in Research and Practice in Information Technology Series Vol. 101 (pp. 159-166). Australia: Australian Computer Society.
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2008 Sun, X., Wang, H., & Li, J. (2008). On the complexity of restricted k-anonymity problem. In 10th Asia Pacific web conference, APWeb 2008. Progress in WWW research and development. Vol. 4976 (pp. 287-296). Berlin/Heidelberg: Springer.
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2008 Wang, H., Yong, J., Li, J., & Peng, M. (2008). Authorization approaches for advanced permission-role assignments. In S. Shen, & W. Weiming (Eds.), Proceedings of the 12th international conference on computer supported cooperative work in design Vol. 1 (pp. 277-282). Beijing, China: IEEE.
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Date Role Research Topic Program Degree Type Student Load Student Name
2025 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Kaylen Smith
2025 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Yi Li
2025 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Thuy Thi Phuong Vu
2025 Principal Supervisor - - Master Full Time Yifan Guo
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Tuyen Vu
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Miss Zhenmin Rao
2023 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Wentao Gao
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Xudong Guo
2023 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Miss Xiaojing Du
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Haolin Wang
2022 Principal Supervisor - Doctor of Philosophy Doctorate Full Time Xiongren Chen
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mrs Sindy Pinero

Date Role Editorial Board Name Institution Country
2022 - ongoing Associate Editor IEEE Intelligent Systems IEEE Computer Society United States

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