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 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
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
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 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, 27 pages.
DOI
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 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, 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 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 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 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 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 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 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 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 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
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 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
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 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 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 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 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 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 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 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 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
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
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 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 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 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 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., 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 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 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.
DOI Scopus29 WoS30 Europe PMC17
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 Scopus20 WoS23 Europe PMC18
2019 Li, J., Zhang, K., Emre, K., & Cui, P. (2019). Introduction to the special section on advances in causal discovery and inference. ACM Transactions on Intelligent Systems and Technology, 10(5), 1-3.
DOI Scopus2 WoS2
2019 Bewong, M., Liu, J., Liu, L., & Li, J. (2019). Privacy preserving serial publication of transactional data. Information Systems, 82, 53-70.
DOI Scopus12
2019 Zhao, Y., Li, J., Qiu, R., & Shi, H. (2019). Growth characterization of intermetallic compound at the Ti/Al solid state interface. Materials, 12(3, article no. 472), 1-11.
DOI
2019 Li, J. Y., Liu, W. M., Zhang, X. F., Ma, Y. B., Chen, M., Qiu, R. F., & Yan, Y. (2019). 高离子传导纳米多孔β-Li₃PS₄固态电解质的湿化学法制备. Cailiao Gongcheng/Journal of Materials Engineering, 47(9), 101-107.
DOI
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 Shi, Z., Mercier-Laurent, E., & Li, J. (2018). Preface. IFIP Advances in Information and Communication Technology, 538, V.
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.
DOI Scopus36 WoS29
2018 Liu, H., Li, X., Li, J., & Zhang, S. (2018). Efficient outlier detection for high-dimensional data. IEEE transactions on systems, man, and cybernetics: systems, 48(12), 2451-2461.
DOI Scopus73
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 Europe PMC6
2018 Hoang, T., Liu, J., Pratt, N., Zheng, V. W., Chang, K. C., Roughead, E., & Li, J. (2018). Authenticity and credibility aware detection of adverse drug events from social media. International Journal of Medical Informatics, 120, 157-171.
DOI Scopus10 WoS5 Europe PMC2
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 Hoang, T., Liu, J., Roughead, E., Pratt, N., & Li, J. (2018). Supervised signal detection for adverse drug reactions in medication dispensing data. Computer Methods and Programs in Biomedicine, 161, 25-38.
DOI Scopus29 WoS23 Europe PMC18
2018 Chow, C. W. K., Liu, J., Li, J., Swlkain, N., Reid, K., & Saint, C. P. (2018). Development of smart data analytics tools to support wastewater treatment plant operation. Chemometrics and intelligent laboratory systems, 177, 140-150.
DOI Scopus31
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 Scopus164 WoS111
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 Scopus39 WoS29
2018 Thuyen, T., Suriyanarayanan, T., Zeng, G., Le, T. D., Liu, L., Li, J., . . . Seneviratne, C. J. (2018). Use of haploid model of Candida albicans to uncover mechanism of action of a novel antifungal agent. Frontiers In cellular and infection microbiology, 8(164), 1-14.
DOI Scopus15 WoS14 Europe PMC12
2018 Hoang, T., Liu, J., Pratt, N., Zheng, V. W., Chang, K. C., Roughead, E., & Li, J. (2018). Authenticity and credibility aware detection of adverse drug events from social media. International journal of medical informatics, 120, 157-171.
DOI Scopus5 WoS4 Europe PMC6
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.
DOI Scopus60 WoS53 Europe PMC64
2018 Reynolds, C., Agrawal, M., Lee, I., Zhan, C., Li, J., Taylor, P., . . . Roos, G. (2018). A sub-national economic complexity analysis of Australia's states and territories. Regional studies, 52(5), 715-726.
DOI Scopus44 WoS38
2018 Zhang, J., Liu, L., Li, J., & Le, T. D. (2018). LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer. Bioinformatics (Oxford, England), 34(24), 4232-4240.
DOI Scopus60 WoS55 Europe PMC53
2018 Le, T., Hoang, T., Li, J., Liu, L., Liu, H., & Hu, S. (2018). A fast PC algorithm for high dimensional causal discovery with multi-core PCs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5), 1483-1495.
DOI Scopus106 WoS91 Europe PMC17
2018 Zhang, Z., Wang, H., Liu, L., & Li, J. (2018). Multi-label relational classification via node and label correlation. Neurocomputing, 292, 72-81.
DOI Scopus10 WoS9
2018 Liu, J., Zhang, X., Fung, B. C. M., Li, J., & Iqbal, F. (2018). Opportunistic mining of top-n high utility patterns. Information Sciences, 441, 171-186.
DOI Scopus17
2018 Zhang, W., Le, T. D., Liu, L., & Li, J. (2018). Estimating heterogeneous treatment effect by balancing heterogeneity and fitness. BMC Bioinformatics, 19(Suppl 19), 12 pages.
DOI Scopus5 WoS4 Europe PMC1
2018 Zhang, J., Le, T. D., Liu, L., & Li, J. (2018). Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer. Briefings in Bioinformatics, 20(4), 1403-1419.
DOI Scopus45 WoS41 Europe PMC38
2017 Le, T. D., Zhang, J., Liu, L., & Li, J. (2017). Computational methods for identifying miRNA sponge interactions. Briefings in Bioinformatics, 18(4), 577-590.
DOI Scopus76 WoS67 Europe PMC70
2017 Liu, H., Liu, L., Le, T. D., Lee, I., Sun, S., & Li, J. (2017). Nonparametric Sparse Matrix Decomposition for Cross-View Dimensionality Reduction. IEEE Transactions on Multimedia, 19(8), 1848-1859.
DOI Scopus18 WoS18
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.
DOI Scopus191 WoS189 Europe PMC178
2017 Zhang, W., Le, T. D., Liu, L., Zhou, Z. H., & Li, J. (2017). Mining heterogeneous causal effects for personalized cancer treatment. Bioinformatics, 33(15), 2372-2378.
DOI Scopus35 WoS33 Europe PMC9
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.
DOI Scopus23 WoS20 Europe PMC15
2017 Zhang, J., Le, T. D., Liu, L., & Li, J. (2017). Identifying miRNA sponge modules using biclustering and regulatory scores. BMC Bioinformatics, 18(Suppl 3), 12 pages.
DOI Scopus18 WoS16 Europe PMC12
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.
DOI Scopus57 WoS41
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.
DOI Scopus5 WoS4 Europe PMC2
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.
DOI
2016 Hoang, T., Liu, J., Pratt, N., Zheng, V. W., Chang, K. C., Roughead, E., & Li, J. (2016). Detecting signals of detrimental prescribing cascades from social media. Artificial Intelligence in Medicine, 71, 43-56.
DOI Scopus15 WoS13 Europe PMC7
2016 Jamali, A., Ferdousi, R., Razzaghi, S., Li, J., Safdari, R., & Ebrahimie, E. (2016). DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discovery Today, 21(5), 718-724.
DOI Scopus85 WoS77 Europe PMC60
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.
DOI Scopus39 WoS16
2016 Ma, S., Li, J., Liu, L., & Le, T. D. (2016). Mining combined causes in large data sets. Knowledge Based Systems, 92, 104-111.
DOI Scopus18 WoS13
2016 Zhang, W., Le, T. D., Liu, L., Zhou, Z. H., & Li, J. (2016). Predicting miRNA targets by integrating gene Regulatory knowledge with Expression profiles. Plos One, 11(4), 19 pages.
DOI Scopus16 WoS22 Europe PMC11
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.
DOI Scopus64 WoS52 Europe PMC39
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.
DOI Scopus27 WoS22 Europe PMC17
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.
DOI Scopus11 WoS10 Europe PMC11
2016 Zhang, J., Le, T. D., Liu, L., He, J., & Li, J. (2016). A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions. Gene, 577(1), 55-64.
DOI Scopus9 WoS10 Europe PMC8
2016 Deng, K., Li, J., Pang, C., & Zhou, X. (2016). Access time oracle for planar graphs. IEEE transactions on knowledge and data engineering, 28(8), 1959-1970.
DOI Scopus1
2016 Zhang, K., Li, J., Bareinboim, E., Schoelkopf, B., & Pearl, J. (2016). Preface to the ACM TIST special issue on causal discovery and inference. ACM transactions on intelligent systems and technology, 7(2, article no. 17), 1-3.
DOI
2016 Li, J., Baig, M. M., Sarowar Sattar, A. H. M., Ding, X., Liu, J., & Vincent, M. W. (2016). A hybrid approach to prevent composition attacks for independent data releases. Information Sciences, 367-368, 324-336.
DOI Scopus29 WoS19
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.
DOI Scopus27 WoS21 Europe PMC23
2015 Sun, Y., Li, J., Liu, J., Chow, C., Sun, B., & Wang, R. (2015). Using causal discovery for feature selection in multivariate numerical time series. Machine Learning, 101(1-3), 377-395.
DOI Scopus85 WoS65
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.
DOI Scopus28 WoS23 Europe PMC22
2015 Xia, W., Heatherly, R., Ding, X., Li, J., & Malin, B. A. (2015). R-U policy frontiers for health data de-identification. Journal of the American Medical Informatics Association, 22(5), 1029-1041.
DOI Scopus20 Europe PMC15
2014 Sun, B. Y., Wang, H. L., Li, W. B., Wang, H. J., Li, J., & Du, Z. Q. (2014). Constructing and combining orthogonal projection vectors for ordinal regression. Neural Processing Letters, 41(1), 139-155.
DOI Scopus7
2014 Sun, Y., Li, J., Liu, J., Sun, B., & Chow, C. (2014). An improvement of symbolic aggregate approximation distance measure for time series. Neurocomputing, 138, 189-198.
DOI Scopus138 WoS105
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.
DOI Scopus22 WoS19 Europe PMC18
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.
DOI Scopus27 WoS25 Europe PMC24
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.
DOI Scopus39 WoS34 Europe PMC26
2014 Li, J., Liu, J., Toivonen, H., Satou, K., Sun, Y., & Sun, B. (2014). Discovering statistically non-redundant subgroups. Knowledge Based Systems, 67, 315-327.
DOI Scopus18 WoS17
2014 Sarowar Sattar, A. H. M., Li, J., Liu, J., Heatherly, R., & Malin, B. (2014). A probabilistic approach to mitigate composition attacks on privacy in non-coordinated environments. Knowledge Based Systems, 67, 361-372.
DOI Scopus26 WoS18 Europe PMC1
2014 Zhang, Z., Li, J., Pan, L., Ye, Y., Zeng, X., Song, T., . . . Wang, E. K. (2014). A novel visualization of DNA sequences, reflecting GC-content. Match: communications in mathematical and in computer chemistry, 72(2), 533-550.
Scopus9
2014 Liu, B., Li, J., & Cairns, M. J. (2014). Identifying miRNAs, targets and functions. Briefings in bioinformatics, 15(1), 1-19.
DOI Scopus485 WoS488 Europe PMC399
2013 Sarowar Sattar, A. H. M., Li, J., Ding, X., Liu, J., & Vincent, M. (2013). A general framework for privacy preserving data publishing. Knowledge Based Systems, 54, 276-287.
DOI Scopus32 WoS24
2013 Li, J., Liu, J., Toivonen, H., & Yong, J. (2013). Effective pruning for the discovery of conditional functional dependencies. Computer Journal, 56(3), 378-392.
DOI Scopus21 WoS10
2013 Liu, J., Ye, F., Li, J., & Wang, J. (2013). On discovery of functional dependencies from data. Data and Knowledge Engineering, 86, 146-159.
DOI Scopus6 WoS6
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.
DOI
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.
DOI Scopus1
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.
DOI Scopus3
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.
DOI Scopus39 WoS40 Europe PMC32
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.
DOI Scopus72 WoS70 Europe PMC50
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.
DOI Scopus12 WoS11
2012 Sun, X., Wang, H., Li, J., & Zhang, Y. (2012). Satisfying privacy requirements before data anonymization. Computer journal, 55(4), 422-437.
DOI Scopus53 WoS41
2012 Liu, J., Li, J., Liu, C., & Chen, Y. (2012). Discover dependencies from data - A review. IEEE Transactions on Knowledge and Data Engineering, 24(2), 251-264.
DOI Scopus130 WoS82
2012 Baig, M. M., Li, J., Liu, J., Ding, X., & Wang, H. (2012). Data privacy against composition attack. Lecture notes in computer science, 7238 LNCS(PART 1), 320-334.
DOI Scopus11
2012 Zhang, J., Liu, B., Jianfeng, H., Ma, L., & Li, J. (2012). Inferring functional miRNA-mRNA regulatory modules in epithelial-mesenchymal transition with a probabilistic topic model. Computers in biology and medicine, 42(4), 428-437.
DOI Scopus8 WoS8 Europe PMC6
2012 Tran, V. A., Clemente, J. C., Nguyen, D. T., Li, J., Dang, X. T., Le, T. T. K., . . . Satou, K. (2012). IMPACT: a novel clustering algorithm based on attraction. Journal of computers, 7(3), 653-665.
DOI
2012 Zhang, Q., Zhang, J., Gao, J., Jianfeng, H., Yan, X., Ma, L., . . . Li, J. (2012). A study of the single point mutation loci in the Hepatitis B Virus sequences via optimal risk and preventive sets with weights. Lecture notes in computer science, 7235, 460-471.
DOI
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.
DOI Scopus2
2012 Zhao, Y., Li, J., Christen, P., & Kennedy, P. J. (2012). Preface. Conferences in Research and Practice in Information Technology Series, 134, vii.
2011 Zhang, J., Jianfeng, H., Ma, L., & Li, J. (2011). ORPSW: A new classifier for gene expression data based on optimal risk and preventive patterns. Journal of computers, 6(6), 1198-1205.
DOI Scopus1
2011 Sun, B. Y., Zhu, Z. H., Li, J., & Linghu, B. (2011). Combined feature selection and cancer prognosis using support vector machine regression. IEEE - ACM Transactions on Computational Biology and Bioinformatics, 8(6, article no. 5654498), 1671-1677.
DOI Scopus36 Europe PMC12
2011 Li, J., Liu, J., Baig, M., & Wong, R. C. W. (2011). Information based data anonymization for classification utility. Data and Knowledge Engineering, 70(12), 1030-1045.
DOI Scopus38 WoS28
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.
DOI Scopus7 WoS6
2011 Sun, X., Wang, H., Li, J., & Zhang, Y. (2011). Injecting Purpose and Trust into Data Anonymisation. Computers and security, 30(5), 332-345.
DOI Scopus36 WoS32
2011 Sun, X., Wang, H., Li, J., & Pei, J. (2011). Publishing anonymous survey rating data. Data mining and knowledge discovery, 23(3), 379-406.
DOI Scopus49 WoS37
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.
DOI Scopus88 WoS84 Europe PMC50
2010 Sun, B. Y., Li, J., Wu, D. D., Zhang, X. M., & Li, W. B. (2010). Kernel discriminant learning for ordinal regression. IEEE transactions on knowledge and data engineering, 22(6), 906-910.
DOI Scopus136
2010 Sun, B. Y., Zhang, X. M., Li, J., & Mao, X. M. (2010). Feature fusion using locally linear embedding for classification. IEEE transactions on neural networks, 21(1), 163-168.
DOI Scopus73 Europe PMC5
2010 Zhang, F., Li, J., Hu, H., & Zhou, H. (2010). A robust ensemble classification method analysis. Advances in Experimental Medicine and Biology, 680, 149-155.
DOI
2010 Li, J., Christen, P., & Kennedy, P. J. (2010). Preface. Conferences in Research and Practice in Information Technology Series, 121.
2009 Khalil, F., Li, J., & Wang, H. (2009). An integrated model for next page access prediction. International Journal of Knowledge and Web Intelligence, 1(1/2), 48-80.
DOI
2009 Li, J., Fu, A. W., & Fahey, P. (2009). Efficient discovery of risk patterns in medical data. Artificial intelligence in medicine, 45(1), 77-89.
DOI Scopus45 Europe PMC12
2009 Wong, R., Li, J., Fu, A. W., & Wang, K. (2009). (a, k)-anonymous data publishing. Journal of Intelligent Information Systems, 33(2), 210-234.
DOI
2009 Liu, B., Li, J., Tsykin, A., Liu, L., Gaur, A., & Goodall, G. (2009). Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy. BMC Bioinformatics, 10(408), 1-19.
DOI Scopus74 WoS60 Europe PMC61
2009 Liu, B., Li, J., & Tsykin, A. (2009). Discovery of functional miRNA-mRNA regulatory modules with computational methods. Journal of Biomedical Informatics, 42(4), 685-691.
DOI Scopus48 WoS44 Europe PMC34
2008 Li, J., Wang, H., Jin, H., & Yong, J. (2008). Current development of k-Anonymous data releasing. Electronic journal of health informatics, 3(1), 1-9.
2008 Leng, J., Li, J., & Jain, L. (2008). A role-orientated BDI framework for real-time multiagent teaming. Intelligent decision technologies, 2(4), 205-217.
DOI Scopus1
2008 Sun, X., Wang, H., Li, J., & Truta, T. M. (2008). Enhanced P-sensitive K-anonymity models for privacy preserving data publishing. Transactions on data privacy. Foundations and technologies, 1(2), 53-66.
WoS49
2008 Li, J., Chi Wing, W. R., Wai Chee, F. A., & Pei, J. (2008). Anonymization by local recoding in data with attribute hierarchical taxonomies. IEEE transactions on knowledge and data engineering, 20(9), 1181-1194.
DOI Scopus45
2008 Leng, J., Li, J., & Jain, L. (2008). A role-oriented BDI framework for real-time multiagent teaming. Intelligent Decision Technologies, 2(4), 205-217.
DOI
2006 Li, J. (2006). On optimal rule discovery. IEEE transactions on knowledge and data engineering.
DOI
2006 Li, J. (2006). Robust rule-based prediction. IEEE transactions on knowledge and data engineering, 18(8), 1043-1054.
DOI
2006 Li, J., & Jones, J. (2006). Using multiple and negative target rules to make classifiers more understandable. Knowledge-Based Systems, 19(6), 438-444.
DOI
2004 Li, J., Shen, H., & Topor, R. (2004). Mining informative rule set for prediction. Journal of Intelligent Information Systems, 22(2), 155-174.
DOI Scopus12
2002 Li, J., Shen, H., & Topor, R. (2002). Mining the optimal class association rule set. Knowledge-Based Systems, 15(7), 399-405.
DOI Scopus55

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.
DOI
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.
DOI
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.
DOI
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.
DOI Scopus8
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.
DOI Scopus1
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.
DOI Scopus3 WoS2
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
Source details - Title: ADMA 2020: Advanced Data Mining and Applications (Vol. 12447 LNAI, pp. 395-409). Singapore: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI WoS1
2015 Li, J., Sattar, S. A., Baig, M. M., Liu, J., Heatherly, R., Tang, Q., & Malin, B. (2015). Methods to mitigate risk of composition attack in independent data publications. In Medical Data Privacy Handbook (pp. 179-200). Switzerland: Springer International Publishing.
DOI Scopus6
2015 Kwashie, S., Liu, J., Li, J., & Ye, F. (2015). Conditional differential dependencies (CDDs). In T. Morzy, P. Valduriez, & L. Bellatreche (Eds.), Lecture Notes in Computer Science (Vol. 9282, pp. 3-17). Switzerland: SPRINGER-VERLAG BERLIN.
DOI Scopus6 WoS10
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.
DOI
2013 Liu, B., Liu, L., Tsykin, A., Goodall, G., Cairns, M., & Li, J. (2013). Discovering functional microRNA-mRNA regulatory modules in heterogeneous data. In U. Schmitz, O. Wolkenhauer, & J. Vera (Eds.), MicroRNA cancer regulation: advanced concepts, bioinformatics and systems biology tools (Vol. 774, pp. 267-290). Springer.
DOI WoS2 Europe PMC3
2013 Liu, B., Liu, L., Tsykin, A., Goodall, G., Cairns, M. J., & Li, J. (2013). Discovering the functional microRNA-mRNA regulatory modules in heterogeneous data. In U. Schmitz, O. Wolkenhauer, & J. Vera (Eds.), Source details - Title: MicroRNA cancer regulation : advanced concepts, bioinformatics and systems biology tools (pp. 267-289). UK: Springer.
DOI
2013 Liu, L., & Li, J. (2013). Building Naïve Bayes Classifiers with High-Dimensional and Small-Sized Data Sets. In Medical Applications of Artificial Intelligence (pp. 115-135). CRC Press.
DOI Scopus1
2011 Sun, X., Wang, H., & Li, J. (2011). Validating privacy requirements in large survey rating data. In N. Bessis, & F. Xhafa (Eds.), Source details - Title: Next Generation Data Technologies for Collective Computational Intelligence (Vol. 352, 1 ed., pp. 445-469). Heidelberg: Springer.
DOI
2010 Baig, M. M., Li, J., Liu, J., Wang, H., & Wang, J. (2010). Privacy protection for genomic data: Current techniques and challenges. In Studies in Computational Intelligence (Vol. 265, pp. 175-193). Berlin: Springer Berlin Heidelberg.
DOI
2010 Leng, J., Lim, C. P., Li, J., Li, D., & Jain, L. (2010). A role-based cognitive architecture for multi-agent teaming. In A. Hakansson, R. Hartung, & N. T. Nguyen (Eds.), Source details - Title: Agent and multi-agent technology for internet and enterprise systems (Vol. 289, pp. 229-255). Germany: Tsinghua University Press & Springer-Verlag.
DOI Scopus2
2010 Zhang, Z., Li, J., Hu, H., & Zhou, H. (2010). On the effectiveness of gene selection for microarray classification methods. In G. Goebel, & R. R (Eds.), Event/exhibition information: Second International Conference, ACIIDS, Hue City, Vietnam, 24/03/2005-26/03/2005
Source details - Title: Lecture notes in computer science (Vol. 5991 LNAI, pp. 300-309). Germany: Springer.
DOI Scopus2

Year Citation
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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.
DOI
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 Machine Learning Research Vol. 267 (pp. 14562-14578).
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.
DOI
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.
DOI Scopus14 WoS8
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.
DOI
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.
DOI Scopus1
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).
Scopus4
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.
DOI Scopus9 WoS6
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).
Scopus10
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.
DOI WoS1
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.
DOI Scopus10
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.
DOI Scopus4 WoS2
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 Scopus17 WoS8
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.
DOI Scopus1 WoS1
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 Scopus6 WoS3
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.
DOI
2023 Le, T. D., Li, J., Ness, R., Triantafillou, S., Shimizu, S., Cui, P., . . . Prosperi, M. (2023). Preface: The 2023 ACM SIGKDD workshop on causal discovery, prediction and decision. In T. Le (Ed.), Proceedings of Machine Learning Research Vol. 218 (pp. 1-2). Netherlands: ML Research Press.
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).
DOI Scopus8 WoS7
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
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 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 WoS4
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 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 Scopus2
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.
DOI Scopus1 WoS1
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.
DOI Scopus64
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.
DOI Scopus1
2021 Le, T. D., Li, J., Cooper, G., Triantafdyllou, S., Bareinboim, E., Liu, H., & Kiyavash, N. (2021). Preface: the 2021 ACM SIGKDD workshop on causal discovery. In Proceedings of Machine Learning Research Vol. 150 (pp. 1-2). US: ML Research Press.
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.
DOI
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.
DOI Scopus14 WoS10
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.
DOI Scopus4 WoS3
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.
DOI Scopus5 WoS5
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.
DOI Scopus10 WoS7
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 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.
DOI Scopus1
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.
DOI Scopus14
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.
DOI Scopus1
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.
DOI Scopus72 WoS41
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 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.
DOI Scopus28
2019 Chow, C., Mussared, A., Fabris, R., Do, P., Liu, J., Li, J., . . . Zappia, L. (2019). Online water quality monitoring and instruments: the voice of experience. In OzWater'18 (pp. 1-7). Australia: Australian Water Association.
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.
DOI Scopus5 WoS4
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.
Scopus1
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 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.
DOI
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.
DOI Scopus9
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 WoS6
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.
DOI Scopus22
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.
DOI
2017 Zhao, Y., Qiu, R., & Li, J. (2017). Microstructure of Al/Ti diffusion couple by resistance spot welding and annealing. In Proceedings Of The 2017 3rd International Forum On Energy, Environment Science And Materials (ifeesm 2017) Vol. 120 (pp. 851-854). Netherlands: Atlantis Press.
DOI
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 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.
DOI Scopus6
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
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
2016 Abdel-Fatao, H., Li, J., Liu, J., & Ashfaqur, R. (2016). An effective spatio-temporal approach for predicting future semantic locations. In M. A. Cheema, W. Zhang, & L. Chang (Eds.), ADC 2016: Databases Theory and Applications Vol. 9877 LNCS (pp. 283-294). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI
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.
2015 Liu, J., Li, J., Xu, S., & Fung, B. C. M. (2015). Secure outsourced frequent pattern mining by fully homomorphic encryption. In S. Madria, & T. Hara (Eds.), Big data analytics and knowledge discovery Vol. 9263 (pp. 70-81). Switzerland: Springer.
DOI Scopus20 WoS13
2015 Abdel-Fatao, H., Li, J., & Liu, J. (2015). STMM: Semantic and temporal-aware Markov chain model for mobility prediction. In C. Zhang, W. Huang, Y. Shi, P. S. Yu, Y. Zhu, Y. Tian, . . . J. He (Eds.), Lecture Notes in Computer Science Vol. 9208 (pp. 103-111). Germany: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus8 WoS8
2015 Abdel-Fatao, H., Li, J., & Liu, J. (2015). Unifying spatial, temporal and semantic features for an effective GPS trajectory-based location recommendation. In M. A. Sharaf, M. A. Cheema, & J. Qi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9093 (pp. 41-53). Germany: SPRINGER-VERLAG BERLIN.
DOI Scopus7 WoS4
2015 Mwintieru Nofong, V., Liu, J., & Li, J. (2015). Efficient mining of non-derivable emerging patterns. In M. A. Sharaf, M. A. Cheema, & J. Qi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9093 (pp. 244-256). Germany: SPRINGER-VERLAG BERLIN.
DOI Scopus1
2015 Kwashie, S., Liu, J., Li, J., & Ye, F. (2015). Efficient discovery of differential dependencies through association rules mining. In M. A. Sharaf, M. A. Cheema, & J. Qi (Eds.), Databases theory and applications Vol. 9093 (pp. 3-15). Germany: SPRINGER-VERLAG BERLIN.
DOI Scopus13 WoS11
2014 Ding, X., Jia, J., Li, J., Liu, J., & Jin, H. (2014). Top-k similarity matching in large graphs with attributes. In S. S. Bhowmick, C. E. Dyreson, C. S. Jensen, M. L. Lee, A. Muliantara, & B. Thalheim (Eds.), DASFAA 2014: International Conference on Database Systems for Advanced Applications Vol. 8422 LNCS (pp. 156-170). US: SPRINGER-VERLAG BERLIN.
DOI Scopus12 WoS8
2014 Liu, H. C., Vincent, M., Liu, J., & Li, J. (2014). Logics for representing data mining tasks in inductive databases. In D. 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. 214-222). US: Springer.
DOI
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.
DOI Scopus4 WoS2
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.
DOI Scopus14 WoS12
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
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.
DOI
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.
DOI Scopus9 Europe PMC2
2013 Ding, X., Yu, Q., Li, J., Liu, J., & Jin, H. (2013). Distributed anonymization for multiple data providers in a cloud system. In Database Systems for Advanced Applications: 18th International Conference, DASFAA 2013, Wuhan, China, April 22-25, 2013. Proceedings, Part I Vol. 7825 LNCS (pp. 346-360). Germany: Springer Berlin Heidelberg.
DOI Scopus12
2013 Jiao, H., Liu, J., Li, J., & Liu, C. (2013). A paradox for trust and reputation in the e-commerce world. In Conferences in Research and Practice in Information Technology Series Vol. 135 (pp. 69-78). US: ACM Digital Library.
Scopus2
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.
DOI
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.
DOI Scopus42 WoS31
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 Scopus29 WoS22
2012 Natarajan, K., Li, J., Liu, J., & Koronios, A. (2012). Rule storage for an efficient rule based inconsistency check. In K. S. Soliman (Ed.), Innovation and Sustainable Competitive Advantage from Regional Development to World Economies Proceedings of the 18th International Business Information Management Association Conference Vol. 4 (pp. 2053-2068). US: INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA.
2012 Jiao, H., Liu, J., Li, J., & Liu, C. (2012). Give rookies a chance: A trust-based institutional online supplier recommendation framework. In D. Gritzalis, S. Furnell, & M. Theoharidou (Eds.), IFIP Advances in Information and Communication Technology Vol. 376 AICT (pp. 400-411). Germany: SPRINGER-VERLAG BERLIN.
DOI Scopus2 WoS2
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.
2011 Jiao, H., Liu, J., Li, J., & Liu, C. (2011). A framework for reputation bootstrapping based on reputation utility and game theories. In G. Wang, S. R. Tate, J. J. Chen, & K. Sakurai (Eds.), Proc 10th IEEE Int Conf on Trust Security and Privacy in Computing and Communications Trustcom 2011 8th IEEE Int Conf on Embedded Software and Systems Icess 2011 6th Int Conf on Fcst 2011 (pp. 344-351). US: IEEE COMPUTER SOC.
DOI Scopus14 WoS10
2011 Baig, M. M., Li, J., Liu, J., & Wang, H. (2011). Cloning for privacy protection in multiple independent data publications. In International Conference on Information and Knowledge Management Proceedings (pp. 885-894). New York: ACM.
DOI Scopus16
2011 Natarajan, K., Li, J., & Koronios, A. (2011). Use rule based to predict dirty values. In Engineering asset management and infrastructure sustainability: proceedings of the 5th world congress on engineering asset management (pp. 693-703). UK: Springer.
DOI
2011 Jiao, H., Liu, J., Li, J., & Liu, C. (2011). Trust for web service composers. In Proceedings of the 12th annual Global Information Technology Management Association world conference (pp. 1-14). US: GITMA.
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
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.
DOI Scopus1
2010 Sun, X., Wang, H., & Li, J. (2010). Satisfying privacy requirements : one step before anonymization. In Advances in knowledge discovery and data mining Vol. 6118 LNAI (pp. 181-editor). Berlin: Springer.
DOI Scopus5
2010 Zhang, F., He, S., Li, J., & Lei, S. (2010). Constraining and summarizing optimal risk and preventive patterns in medical data. In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (pp. 1-4). US: IEEE Press.
DOI Scopus1
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.
2009 Sun, X., Wang, H., & Li, J. (2009). Injecting purpose and trust into data anonymisation. In C. Cheung, & D. David (Eds.), Proceeding of the 18th ACM conference on information and knowledge management (pp. 1541-1543). US: ACM.
DOI Scopus18
2009 Natarajan, K., Li, J., & Koronios, A. P. (2009). Detecting mis-entered values in large data sets. In Proceedings of the 4th World Congress on Engineering Asset Management (pp. 805-812). UK: Springer.
DOI Scopus1
2009 Natarajan, K., Li, J., & Koronios, A. (2009). Data mining techniques for data cleaning. In Engineering asset management: proceedings of the 4th World Congress on Engineering Asset Management (WCEAM) (pp. 796-804). UK: Springer-Verlag.
DOI Scopus22
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 Wang, J., Wang, K., & Li, J. (2009). Finding irredundant contained rewritings of tree pattern queries using views. In L. Qing (Ed.), Lecture notes in computer science : advances in data and web management Vol. 5446 (pp. 113-125). Netherlands: Springer.
DOI Scopus4
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.
DOI Scopus15 WoS7
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.
DOI WoS8
2008 Leng, J., Li, J., & Jain, L. (2008). A role-based framework for multi-agent teaming. In Knowledge-based intelligent and information engineering (pp. 642-649). Germany: Springer-Verlag.
DOI
2008 Sun, X., Wang, H., Truta, T. M., Li, J., & Li, P. (2008). (p+,a) -sensitve k-anonymity: a new enhanced privacy protection model. In Proceedings of the IEEE 8th international conference on computer and information techonology, CIT 2008 (pp. 59-64). Sydney, Australia: IEEE.
DOI Scopus33 WoS15
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.
DOI Scopus2
2008 Huang, X., Yong, J., Li, J., & Gao, J. (2008). Prediction of student actions using weighted Markov models. In Proceedings of 2008 IEEE international symposium on IT in medicine and education (pp. 154-159). Xiamen, China: IEEE.
DOI Scopus5
2008 Hong, H., Li, J., Wang, H., Daggard, G., & Wang, L. Z. (2008). Robustness analysis of diversified ensemble decision tree algorithms for microarray data classification. In Proceedings of the seventh international conference on machine learning and cybernetics Vol. 1 (pp. 115-120). Kunming, China: IEEE.
DOI WoS4
2008 Khalil, F., Li, J., & Wang, H. (2008). Integrating recommendation models for improved web page prediction accuracy. In Proceedings of the thirty-first Australasian computer science conference , ACSC 2008 Vol. 74 (pp. 91-100). Darlinghurts, Australia: Australian Computer Society Inc..
Scopus26
2008 Yong, J., Li, J., & Wang, H. (2008). Portable devices of security and privacy preservation for e-learning. In S. Shen, & W. Weiming (Eds.), Proceedings of the 12th international conference on computer supported cooperative work in design 2008 Vol. 2 (pp. 1029-1034). Beijing, China: IEEE.
DOI Scopus2
2008 Sun, X., Wang, H., & Li, J. (2008). Priority driven K-anonymisation for privacy protection. In Proceedings of the seventh Australasian data mining conference, AusDM 2008 Vol. 87 (pp. 73-78). Australia: Australian Computer Society.
Scopus3
2008 Bu, Y., Fu, A. W., Wong, R., Chen, L., & Li, J. (2008). Privacy preserving serial data publishing by role composition. In Proceedings of the very large data bases endowment, VLDB 08 Vol. 1 (pp. 845-856). New Zealand: VLDB Endowment, ACM.
DOI Scopus70 WoS37
2008 Petrus, K., Li, J., & Fahey, P. (2008). Comparing decision tree and optimal risk pattern mining for analysing emergency ultra short stay unit data. In Proceedings of the seventh international conference on machine learning and cybernetics Vol. 1 (pp. 234-239). Kunming, China: IEEE.
DOI Scopus2
2008 Sun, X., Wang, H., & Li, J. (2008). L-Diversity based dynamic update for large time-evolving microdata. In Proceedings of the 21st Australasian joint conference on artificial intelligence, AI 2008. LNCS series vol.5360 Vol. 5360 LNAI (pp. 461-469). Berlin/Heidelberg: Springer.
DOI Scopus7
2008 Sun, X., Wang, H., & Li, J. (2008). On the complexity of restricted k-anonymity problem. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 4976 LNCS (pp. 287-296). Springer Berlin Heidelberg.
DOI Scopus12
2008 Sun, X., Wang, H., & Li, J. (2008). <i>L</i>-Diversity Based Dynamic Update for Large Time-Evolving Microdata. In W. Wobcke, & M. Zhang (Eds.), AI 2008: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS Vol. 5360 (pp. 461-+). NEW ZEALAND, Auckland Univ Technol, Auckland: SPRINGER-VERLAG BERLIN.
WoS5
2008 Leng, J., Li, J., & Jain, L. C. (2008). A role-based framework for multi-agent teaming. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 5179 LNAI (pp. 642-649). Springer Berlin Heidelberg.
DOI Scopus4
2007 Khalil, F., Li, J., & Wang, H. (2007). Integrating Markov model with clustering for predicting web page accesses. In AusWeb07 : the Thirteenth Australasian Word Wide Web Conference. NSW, Australia: Southern Cross University.
2007 Li, J., Huang, X., Selke, C., & Yong, J. (2007). A fast algorithm for finding correlation clusters in noise data. In Advances in knowledge discovery and data mining : 11th Pacific-Asia conference, PAKDD 2007, Nanjing, China, May 22-25, 2007 ; proceedings (pp. 639-647). Germany: Springer.
DOI
2006 He, H., Jin, H., Chen, J., McAullay, D., Li, J., & Fallon, T. (2006). Analysis of breast feeding data using data mining methods. In Australian data mining conference. Sydney: ACS Press.
2006 Hu, H., Li, J., Wang, H., & Daggard, G. (2006). Combined gene selection methods for microarray data analysis. In Proceedings of 10th International Conference Knowledge-Based Intelligent Information and Engineering Systems (pp. 976-983). Berlin: Springer.
DOI
2006 Li, J., Wong, R., Fu, A. W. C., & Pei, J. (2006). Achieving k-anonymity by clustering in attribute hierarchical structures. In 8th International Conference on Data Warehousing and Knowledge Discovery (DaWak). Krakow, Poland: Springer.
DOI
2006 Hu, H., Li, J., Plank, A., Wang, H., & Daggard, G. (2006). A comparative study of classification methods for microarray data analysis. In Proceedings of Australian Data Mining conference. Sydney: ACS Press.
2006 Wang, H., Li, J., Addie, R., Dekeyser, S., & Watson, R. (2006). A framework for role-based group delegation in distributed environments. In Australasian computer science conference. Sydney: ACS Press.
2006 Wong, R., Li, J., Fu, A. W. C., & Wang, K. (2006). (alpha, k)-anonymity: an enhanced k-anonymity model for privacy-preserving data publishing. In Proceedings of the twelfth ACMKDD international conference on knowledge discovery and data mining (KDD). New York: ACM Press.
DOI
2006 Li, J., & Jones, J. (2006). Classification using multiple and negative target rules. In Proceedings of 10th International Conference Knowledge-Based Intelligent Information and Engineering Systems (pp. 212-219). Berlin: Springer.
DOI
2006 Khalil, F., Li, J., & Wang, H. (2006). A framework of combining Markov model with association rules for predicting web page accesses. In Proceedings of Australian data mining conference. Sydney: ACS Press.
2005 Li, J., Fu, A. W. C., He, H., Chen, J., Jin, H., McAullay, D., . . . Kelman, C. (2005). Mining risk patterns in medical data. In Proceeding of the eleventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 776-781). New York: ACM Press.
DOI
2005 Hu, H., & Li, J. (2005). Using association rules to make rule-based classifiers robust. In Proceedings of sixteenth Australasian database conference (ADC). Sydney: ACS Press.
2005 Chen, X., Li, J., Daggard, G., & Huang, X. (2005). Finding similar patterns in microarray data. In Proceedings of eighteenth Australian joint conference on artificial intelligence (pp. 1272-1276). Berlin: Springer.
DOI
2005 Chen, J., He, H., Li, J., Jin, H., McAullay, D., Williams, G., . . . Kelman, C. (2005). Representing association classification rules mined from health data. In Lecture notes in computer science (pp. 1225-1231). Germany: Springer.
DOI
2003 Gu, L., Li, J., He, H., Williams, G., Hawkins, S., & Kelman, C. (2003). Association rule discovery with unbalanced class distributions. In Proceedings of sixteenth Australian joint conference on artificial intelligence (pp. 221-232). Berlin: Springer.
DOI
2003 Li, J., & Zhang, Y. (2003). Direct interesting rule generation. In Proceedings of the third IEEE international conference on data mining (ICDM) (pp. 155-162). US: CS Press.
DOI
2002 Li, J., Topor, R., & Shen, H. (2002). Construct robust rule sets for classification. In Proceedings of the eighth ACMKDD international conference on knowledge discovery and data mining (KDD) (pp. 564-569). New York: ACM Press.
DOI Scopus17
2001 Li, J., Shen, H., & Topor, R. (2001). Mining optimal class association rule set. In Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science Vol. 2035 (pp. 364-375). Springer Berlin Heidelberg.
DOI Scopus12
2001 Li, J., Shen, H., & Topor, R. (2001). Mining the smallest association rule set for predictions. In Proceedings of the IEEE International Conference on Data Mining (ICDM, 2001) (pp. 361-368). USA: IEEE Computer Society.
DOI Scopus34
1999 Li, J., Shen, H., & Topor, R. (1999). An adaptive method of numerical attribute merging for quantitative association rule mining. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 1749 (pp. 41-52). Springer Berlin Heidelberg.
DOI Scopus8

  • 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

  • SmartSat 3-07s: Satellite image-based smoke detection for bush fire detection - Liang Zhao, SmartSat CRC, 05/10/2020 - 04/04/2024

  • SmartSat P2-38: Energy-efficient on-board AI for early fire-smoke detection, SmartSat CRC, 01/03/2022 - 30/10/2023

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

  • D2D CRC Limited Scholarship, D2D CRC Limited, 16/10/2017 - 17/10/2020

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

  • D2D CRC Limited Scholarship, D2D CRC Limited, 28/11/2016 - 30/11/2019

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

  • Online Monitoring Guidance Manual Incorporating Decision Support Tools for Superior Process Performance WRA Project Number: 1075-13, Water Research Australia, 17/11/2014 - 31/12/2017

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

  • Stage 4 - Economic Complexity Analysis, SA Dept of Innovation and Skills (formerly DSD), 01/06/2016 - 30/06/2017

Courses I teach

  • COMP 4008 Data and Web Mining (2025)
  • INFT 3044 Research Directions in ICT (2025)
  • INFT 3047 Industry Research Directions in ICT (2025)
  • COMP 4008 Data and Web Mining (2024)
  • INFT 3044 Research Directions in ICT (2024)

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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