Xiaohui Tao, PhD
Dr. Xiaohui Tao is a Senior member of IEEE and ACM, an active researcher in AI, and Associate Professor (Computing) in School of Sciences, University of Southern Queensland (USQ), Australia. His research interests include data analytics, machine learning, knowledge engineering, information retrieval, and health informatics. During his research career, Tao gained a wealth of knowledge and experience in dealing with massive data sets and delivering solutions to complex research problems. He developed many innovative models, methods and systems, such as a multi-disease recommender system, a clinic decision support system for personalized and evidence-based medicine, a heterogeneous information graph model for health risk prediction, an algorithm to detect potential mental issues using sentiment analysis and natural language processing techniques, and an ontology learning and mining model for personalized information gathering, and made contributions to the areas such as Knowledge Engineering, Text Mining and Information Retrieval and Health informatics. The research outcomes have been published on many top-tier journals (e.g., IEEE TKDE, IPM, KBS and ESWA) and conferences (e.g., ICDE, CIKM, PAKDD and WISE). A/Prof. Tao is an Endeavour Research Fellow in 2015-16 and was awarded with Research Award by Department of Mathematics and Computing, USQ in 2012 and the Dean’s Award for Academic Excellence by Faculty of Science and Technology at QUT in 2009.
Dr. Tao has been active in professional services. He has served PC Chair in WI ‘17 and ‘18 and BESC ‘18. He has been an editor or guest editor in many journals including WIJ, WWWJ, and INFFUS, and also been a regular reviewer in many top-ranking journals such as TKDE, TPDS, Neural Networks, and Knowledge Based Systems. Dr. Tao has also been actively participating in tertiary education ever since 2005 and taught a variety of IT/IS subjects. Currently, he is leading the Data Mining and Analytics (DMA) Group in School of Sciences, USQ and is the Principal Supervisor of a number of PhD and Research Master students.
Text mining, information retrieval, natural language processing, machine learning, data analytics, recommender systems, semantic web, health informatics
Selected Research Projects (recruiting HDR students)
- Computational Social Science for Online Mental Health using Artificial Intelligence
Many people are suffering from mental issues without knowledge of it. As a result, they are unable to access to appropriate helps. Finding and helping these people have motivated us in the research proposed in this project. It will model the behaviour of online social network users by analysing their expressions using natural language processing and machine learning techniques, and alert potential mental issues adopting data mining techniques like outlier detection. A knowledge base conceptualising mental health domain knowledge will provide foundation to these tasks. With the outcome of the work, clinical decision support systems can be designed to assist psychologists and social workers in diagnose and help people with mental issues at early stage; tools like mobile apps can be developed to help guardians like parents to keep an eye on their children’s mental health proactively without breaching their privacy. People suffering from mental issues can also benefit from the tools by monitoring their own mental health easily, so that they could pull back at early stage and avoid falling into more severe circumstances if anything wrong is happening. The proposed research will make potential theoretical contributions to deepening our understandings of mental health, as well methodological contributions to knowledge engineering, natural language processing and data analytics.
Artificial Intelligence and Big Data Analytics for P4 Medicine
Recent successes in Biotechnology and Artificial Intelligence have been driving the transformation of medical practice from traditional untargeted, reactive and experience-based to targeted, proactive and evidence-based. P4 (Predictive, Preventative, Personalised and Participatory) medicine will provide cost-effective disease care, reduce the incidence of diseases and replicate the innovation cycle of systems medicine on a large scale, and is believed “a revolution of medicine / healthcare practice”. This research is focused on predictive and personalised medicine by predicting potential diseases based on patient’s personal health status using Machine Learning techniques. It will further support physicians’ clinical decisions by providing prescription re-check and suggesting treatment plans using knowledge bases and information retrieval techniques. To achieve these goals, study of massive data in heterogeneous types is essential. The research will help develop our capability of proactive and evidence-based medicine and help design clinical decision support systems.
Machine Learning and Knowledge Engineering for Recommender Systems
Recommender systems predict the interests and preferences latently held by a user and try to deliver the user targeted and personalised recommendations on products and services. Using collaborative information filtering and user profiling techniques, recommender systems have been greatly successful and widely used in many large organisations including Netflix, Amazon, Facebook, and LinkedIn. However, from research perspective many challenges stand still and deserve great endeavour from the research community, such as Cold Start, Information mismatching and overloading, Scalability, Diversity, Privacy, and shilling attacks, etc. This research project aims at making a breakthrough in recommender systems adopting stat-of-the-art techniques in Machine Learning (e.g., deep learning) and Knowledge Engineering (e.g., knowledge graph).
- Knowledge-based User Concept Modelling for Personalised Information Gathering in Big Data Era
User concept models are formal description and specification of user background knowledge. In their brains, users implicitly possess a concept model, which is generated from their background knowledge. While this concept model cannot be proven in laboratories, many knowledge engineers have observed it in user behaviour. When users read through a document, they can easily determine whether or not it is of their interest, on the basis of a judgement that arises from their implicit concept models. Therefore, there exists a hypothesis if a user’s concept model can be simulated, we can understand how a decision (e.g., whether a document is interesting) is made, and thus, we can infer user information needs by analysing the existing concepts in simulated user concept model. This study focuses on user concept models in the personalised information gathering considering challenges presented in Big Data era. The thesis project will make potential theoretical contributions to knowledge advancement in knowledge engineering and cognitive science, as well methodological contributions to text mining, information retrieval, and data format to help deal with Big Data challenges.
- Ning Zhong, Jianhua Ma, Jiming Liu, Runhe Huang, and Xiaohui Tao (Eds.). Wisdom Web of Things (W2T). 2016, Springer [ISBN 978-3-319-44198-6][DOI: 10.1007/978-3-319-44198-6]
- Harleen Kaur and Xiaohui Tao (Eds.). ICTs and the Millennium Development Goals - A United Nations Perspective. 2014, Springer. [ISBN 978-1-4899-7438-9] [DOI: 10.1007/978-1-4899-7439-6]
Selective Book Chapters
- Raid Lafta, Ji Zhang, Xiaohui Tao, Yan Li, Mohammed Diykh and Jerry Chun-Wei Lin. A Structural Graph-Coupled Advanced Machine Learning Ensemble Model for Disease Risk Prediction in a Telehealthcare Environment. In: Roy S., Samui P., Deo R., Ntalampiras S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore, 2018 [DOI: https://doi.org/10.1007/978-981-10-8476-8 18]
- Jianhui Chen, Jian Han, Yue Deng, Han Zhong, Ningning Wang, Youjun Li, Zhijiang Wan, Taihei Kotake, Dongsheng Wang, Xiaohui Tao, and Ning Zhong. Multi-level Big Data Content Services for Mental Health Care. Wisdom Web of Things (W2T), pages 155-180, 2016, Springer. [DOI: https://doi.org/10.1007/978-3-319-44198-6 7]
Selective Journal Articles
- Thomas Body, Xiaohui Tao, Yuefeng Li, Lin Li, and Ning Zhong, Using Back-and-Forth Translation to Create Artificial Augmented Textual Data for Sentiment Analysis Models. Expert Systems With Applications, April 2021 [doi: 10.1016/j.eswa.2021.115033][Q1]
- Xiaohui Tao, Thanveer Basha Shaik, Niall Higgins, Raj Gururajan, Xujuan Zhou, Remote Patient Monitoring using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities, Sensors, MDPI, 21(3), 776, 2021. [doi:10.3390/s21030776][Q1]
- Ru Wang, Lin Li, Xiaohui Tao, Peipei Wang and Peiyu Liu, Trio-based collaborative multi-view graph clustering with multiple constraints, Information Processing and Management, Elsevier, 58(3), 2021 [doi: 10.1016/j.ipm.2020.102466][Q1, SNIP 3.199]
- Xieling Chen, Xiaohui Tao, Fu Lee Wang and Haoran Xie, Global research on artificial intelligence technologies-enhanced human electroencephalogram analysis, Neural Computing and Applications, Springer, 2021 [doi: 10.1007/s00521-020-05588-x][Q1]
- Xiaohui Tao, Oliver Chi, Patrick J. Delaney, Lin Li and Jiajin Huang. Detecting Depression using an Ensemble Classifier Based on Quality of Life Scales. Brain Informatics, Springer, 8, 2, 2021. [SNIP: 2.333, doi: 10.1186/s40708-021-00125-5][Q1]
- Zaher Mundher Yaseen, Sujan Ghimire; Ravinesh C Deo; Ji Zhang; Xiaohui Tao. Deep Short-Term Stream ow Prediction: An integrated Approach of Convolutional Neural Network and Long Short-Term Memory Networks. To appear in Scientific Reports, 2021. [Q1]
- Lin Li, Lingyun Zhao, Peiran Nai, and Xiaohui Tao Charge prediction modeling with interpretation enhancement driven by double-layer criminal system. World Wide Web, Feb 2021. [doi:10.1007/s11280-021-00873-8][Q2]
- Tamara Abdulmunim Abduljabbar, Xiaohui Tao, Ji Zhang, Xujuan Zhou, Lin Li, Yi Cai, A Survey of Privacy Protection using Blockchain in Recommender Systems: Current State and Open Issues. Computer Journal, Oxford, March 2021. [doi: 10.1093/comjnl/bxab065][Q2]
- Guipeng Zhang, Haoran Xie, Zhenguo Yang, Xiaohui Tao, and Wenyin Liu, BDKM: A blockchain-based secure deduplication scheme with reliable key management, Neural Processing Letters, 04 March 2021. [doi: 10.1007/s11063-021-10450-9][Q2]
- Wee Pheng Goh, Xiaohui Tao, Ji Zhang, Jianming Yong, Feature-based Learning in Drug Prescription System for Medical Clinics, Neural Processing Letters, 52, pages 1703–172, 2020 [doi: 10.1007/s11063-020-10296-7][Q2]
- Christopher Kok, V Jahmunah, Shu Lih Oh , Xujuan Zhou, Raj Gururajan, Xiaohui Tao, Kang Hao Cheong, Rashmi Gururajan, and U Rajendra Acharya, Automated Prediction of Sepsis Using Temporal Convolutional Network, Computers in Biology and Medicine, Volume 127, December 2020, 103957, 2020 [doi: 10.1016/j.compbiomed.2020.103957][Q1]
- Xieling Chen, Xinxin Zhang, Haoran Xie, Xiaohui Tao, Fu Lee Wang, Nengfu Xie and Tianyong Hao, A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research, Multimedia Tools and Applications, 11 June 2020 [doi: 10.1007/s11042-020-09062-7][Q1]
- Ji Zhang, Leonard Tan, Xiaohui Tao, Thuam Pham and Bing Chan, Rational Intelligence Recognition in Online Social Networks - A Survey, Computer Science Review, volume 35, Feb 2020 [doi: 10.1016/j.cosrev.2019.100221][Q1]
- Raid Luaibi Lafta, Ji Zhang and Xiaohui Tao, A General Extensible Learning Approach for Multi-Disease Recommendations in a Telehealth Environment, Pattern Recognition Letters, Elsevier, Volume 132, page 106-114, April 2020 [https://doi.org/10.1016/j.patrec.2018.11.006] [Q1]
- Moloud Abdar, Mariam Zomorodi-Moghadam, Xujuan Zhou, Raj Gururajan, Xiaohui Tao, Prabal D Barua and Rashmi Gururajan, A new nested ensemble technique for automated diagnosis of breast cancer, Pattern Recognition Letters, Elsevier, Volume 132, page 123-131, April 2020 [https://doi.org/10.1016/j.patrec.2018.11.004] [Q1]
- Xiaohui Tao, Thuan Pham, Ji Zhang, Jianming Yong, Wee Pheng Goh, Wenping Zhang, Yi Cai, Mining Health Knowledge Graph for Health Risk Prediction, World Wide Web, Springer, 2020 [https://doi.org/10.1007/s11280-020-00810-1][Q2]
- Xiaohui Tao, Wee Pheng Goh, Ji Zhang, Jianming Yong, Elizabeth Z. Goh, Xueling Oh, Mobile-based Learning of Drug Prescription for Medical Education using Artificial Intelligence Techniques, International Journal of Mobile Learning and Organisation, InderScience, 2020 [Q1]
- Thuan Pham, Xiaohui Tao, Ji Zhang and Jianming Yong, Constructing a Knowledge-based Heterogeneous Information Graph for Medical Health Status Classification, International Journal of Health Information Science and Systems, volume 8, Article number: 10 (2020). [https://doi.org/10.1007/s13755-020-0100-6][Q1]
- Da Ren, Pengfei Zhang, Qing Li, Xiaohui Tao, Junying Chen and Yi Cai, A Hybrid Representation Based Similar Component Extraction, Neural Computing and Applications (2020) [https://doi.org/10.1007/s00521-020-04818-6]
- Ji Zhang, Leonard Tan, and Xiaohui Tao. On Relational Learning and Discovery in Social Networks: A Survey, International Journal of Machine Learning and Cybernetics, 10(8): 2085-2102, 2019. [https://doi.org/10.1007/s13042-018-0823-8][Q1]
- Ji Zhang, Raid Luaibi Lafta, Xiaohui Tao, Yan Li, Fulong Chen, Yonglong Luo, and Xiaodong Zhu. Coupling a Fast Fourier Transformation with a Machine Learning Ensemble Model to Support Recommendations for Heart Disease Patients in a Telehealth Environment, IEEE Access, vol. 5, pp. 10674-10685, (2017). [https://doi.org/10.1109/ACCESS.2017.2706318][Q1, SNIP: 3.601]
- Omar Ali, Jeffrey Soar, Jianming Yong and Xiaohui Tao. Factors to be considered in Cloud Computing Adoption. Web Intelligence, an International Journal, 14(4): 309-323 (2016). [https://doi.org/10.3233/WEB-160347]
- Wee Phong Goh, Xiaohui Tao, Ji Zhang, and Jianming Yong. Decision Support Systems for Adoption in Dental Clinics: A Survey. Knowledge-Based Systems, Elsevier, 104, 195-206 (2016).[https://doi.org/10.1016/j.knosys.2016.04.022][Q1; SNIP: 2.757; SJR: 2.190; IF: 2.947; 5-yr IF: 3.011]
- Ji Zhang, Xiaohui Tao, and Hua Wang. Outlier Detection from Large Distributed Databases. World Wide Web, Springer, 17(4):539-568, 2014. [https://doi.org/10.1007/s11280-013-0218-4] [Q2]
- Xiaohui Tao, Yuefeng Li, and Ning Zhong. A Personalised Ontology Model for Web Information Gathering. IEEE Transactions on Knowledge and Data Engineering, 23(4):496-511 (2011). [https://doi.org/10.1109/TKDE.2010.145][Q1]
Selective Conference Papers (2016-)
- Kaixi Hu, Lin Li, Qing Xie, Jianquan Liu and Xiaohui Tao, What is Next when Sequential Prediction Meets Implicitly Hard Interaction? In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM) 2021, 1-5 November 2021, Online and Gold Coast, Queensland, Australia
- Yuquan Peng, Lin Li, Qing Xie, and Xiaohui Tao, Learning Cooperative Max-Pressure Control by Leveraging Downstream Intersections Information for Trafic Signal Control, accepted by The 5th APWeb-WAIM International Joint Conference on Web and Big Data (APWEb-WAIM), August 23-25, 2021, Guangzhou, China.
- Zongxi Li, Xinhong Chen, Haoran Xie, Qing Li, and Xiaohui Tao, EmoChannelAttn: Exploring Emotional Construction Towards Multi-Class Emotion Classification, accepted by the 2020 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020), Melbourne, Australia, 2020 [Best Research Paper, CORE B, acceptance rate: 25%]
- Peiran Nai, Lin Li and Xiaohui Tao, A Densely Connected Encoder Stack Approach for Multi-type Legal Machine Reading Comprehension, accepted by International Conference on Web Information Systems Engineering (WISE), 2020. [Best Student Paper Award]
- Zhaoyang Li, Lin Li and Xiaohui Tao, A Two-Stream Graph Convolutional Neural Network for Dynamic Trafic Flow Forecasting, accepted by 32nd International Conference on Tools with Artificial Intelligence(ICTAI), 2020
- Ru Wang, Lin Li, Peipei Wang, Xiaohui Tao, Peiyu Liu, Feature-aware unsupervised learning with joint variational attention and automatic clustering, accepted by the 25th International Conference on Pattern Recognition (ICPR), 2020.
- Abhishek Mahalle, Jianming Yong, and Xiaohui Tao. Regulatory Challenges and Mitigation for Account Services Offered by FinTech. Accepted for the 2020 24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD 2020), Dalian, China
- Abhishek Mahalle, Jianming Yong, and Xiaohui Tao. Challenges and Mitigation for Application Deployment over SaaS Platform in Banking and Financial Services Industry. Accepted for the 2020 24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD 2020), Dalian, China
- Ji Zhang, Leonard Tan, Xiaohui Tao, Dianwei Wang, Josh Jia-Ching Ying and Xin Wang, Learning Relational Fractals For Deep Knowledge Graph Embedding In Online Social Networks, Web Information Systems Engineering (WISE) 2019, Springer International Publishing, pp. 660-674, 2019 [https://doi.org/10.1007/978-3-030-34223-4_42][Best Runner-up Paper Award]
- Wee Pheng Goh, Xiaohui Tao, Ji Zhang, and Jianming Yong, Personalised Drug Prescription for Dental Clinics Using Word Embedding, In: U L., Yang J., Cai Y., Karlapalem K., Liu A., Huang X. (eds) Web Information Systems Engineering. WISE 2019. Communications in Computer and Information Science, vol 1155. Springer, Singapore [https://doi.org/10.1007/978-981-15-3281-8_5]
- Ji Zhang, Leonard Tan, Xiaohui Tao, Hongzhou Li, Fulong ChenYonglong Luo, SLIND+: Stable LINk Detection, In: U L., Yang J., Cai Y., Karlapalem K., Liu A., Huang X. (eds) Web Information Systems Engineering. WISE 2019. Communications in Computer and Information Science, vol 1155. Springer, Singapore [https://doi.org/10.1007/978-981-15-3281-8_8]
- Abhishek Mahalle, Jianming Yong, and Xiaohui Tao. Ethics of IT Security Team for Cloud Architecture Infrastructure in Banking and Financial Services Industry. 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal, 2019, pp. 506-511.[https://doi.org/10.1109/CSCWD.2019.8791928]
- Abhishek Mahalle, Jianming Yong, and Xiaohui Tao. Insider Threat and Mitigation for Cloud Architecture Infrastructure in Banking and Financial Services Industry. 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal, 2019, pp. 16-21.[https://doi.org/10.1109/CSCWD.2019.8791906]
- Ji Zhang, Leonard Tan, Xiaohui Tao, Jerry Chun-Wei Lin, Hongzhou Li and Liang Chang. On Link stability Detection for Online Social Networks. In Proceedings of 28th International Conference on Database and Expert Systems Applications (DEXA 2018), pp 320-335. September 3-6, 2018, Germany.
- Raid Lafta, Ji Zhan, Xiaohui Tao, Jerry Chun-Wei Lin, Fulong Chen, Yonglong Luo and Xiaoyao Zheng. A Recommender System with Advanced Time Series Medical Data Analysis for Diabetes Patients in a Telehealth Environment. In Proceedings of 28th International Conference on Database and Expert Systems Applications (DEXA 2018), pp 185-192, September 3-6, 2018, Germany.
- Ji Zhang, Leonard Tan, Xiaohui Tao, Xiaoyao Zheng, Yonglong Luo, and Jerry Chun-Wei Lin. SLIND: Identifying Stable Links in Online Social Networks. The 23rd International Conference on Database Systems for Advanced Applications (DASFFA 2018), pp 813-816, May 21-24, 2018, Gold Coast, Australia.
- Abhishek Mahalle, Jianming Yong and Xiaohui Tao. Data Privacy and System Security for Banking and Financial Services Industry based on Cloud Computing Infrastructure. Accepted by the 2018 22nd IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD 2018), May 9-11, 2018, Nanjing, China
- Xujuan Zhou, Xiaohui Tao, Mostafa Rahman, and Ji Zhang. Coupling Topic Modelling in Opinion Mining for Social Media Analysis. In Proceeding of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence, Leipzig, Germany, 2017.
- Wee Pheng Goh, Xiaohui Tao, Ji Zhang, and Jianming Yong. Mining Drug Properties for Decision Support in Dental Clinics. In Proceedings of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 375-387, 2017, Jeju, South Korea.
- Raid Lafta, Ji Zhang, Xiaohui Tao, Yan Li, Wessam Abbas, Yonglong Luo, Fulong Chen and Vincent S. Tseng. A fast Fourier transform-coupled machine learning-based ensemble model for disease risk prediction using a real-life dataset. In Proceedings of the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp 654-670, 2017.
- Xiaohui Tao, Xujuan Zhou, Ji Zhang, and Jianming Yong. Sentiment Analysis for Depression Detection on Social Networks. In Proceedings of the 12th International Conference on Advanced Data Mining and Applications (ADMA ‘16), pp. 807-810, 2016.
- Raid Lafta, Ji Zhang, Xiaohui Tao, Yan Li, and Vincent S. Tseng. IRS-HD: an Intelligent Personalized Recommender System for Heart Disease Patients in a Tele-health Environment. In Proceedings of the 12th International Conference on Advanced Data Mining and Applications (ADMA ‘16), pp. 803-806, 2016.
- Ali A. Al-kharaz, Xiaohui Tao, Ji Zhang, and Raid Lafta. Adopting Hybrid Descriptors to Recognise Leaf Images for Automatic Plant Specie Identification. In Proceedings of the 12th International Conference on Advanced Data Mining and Applications (ADMA ‘16), pp. 219-233, 2016. (Acceptance Rate 17%)
- Mehdi Gheisari, Mojtaba Fazli, Yongrui Qin, Jianming Yong, Xiaohui Tao, Ji Zhang, and Haifeng Shen. NSSSD:A New Semantic hierarchical Storage for Sensor Data. In Proceedings of the 20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 174-179, 2016.
HDR Supervision (completed theses)
- Dr. G. Wang (PhD), Domination Problems in Social Networks, 2014
- Dr. M. M. Albathan (PhD), Enhancement of Relevant Features for Text Mining, 2015
- Dr. R. L. Lafta (PhD), An Intelligent Recommender System based on Predictive Risk Analysis in Health Medical Domain, 2018
- Dr. L. Tan (PhD), Event Prediction Through Structural Intelligence in Online Social Networks, 2020
- Dr. W. P. Goh (PhD), Data Mining for Precision Medicine in Clinical Decision Support, 2020
- Dr. T. Pham (PhD), Knowledge Discovery for Health Risk Prediction, 2020
- CSC3600 ICT Professional Project
- Avaliable projects(per requested)
- CSC8600 Advanced ICT Professional Project
- Avaliable projects(per requested)
- MSC8001 Research Methodology
- Avaliable research projects (per requested)
- MSC8002 Research Dissertation
- MSC8003 Industry Based Research Practice I
- Avaliable research projects (per requested)
- MSC8004 Industry Based Research Practice II
- CSC3600 ICT Professional Project, USQ
- CSC8600 Advanced ICT Professional Project, USQ
- CSC8003 Machine Learning, USQ
- MSC8001 Research Project (Methodology), USQ
- MSC8003 Industry Based Research Practice I, USQ
- MSC8004 Industry Based Research Practice II, USQ
|IT Project and Capstone
||ICT Professional Project
Core Project Management
Core Project Implementation
|Programming and BI
||Enterprise Data Mining
Computing Complementary Studies
||Agent-based Software engineering
Software Engineering Studies
Modeling Design and Analysis
Discrete Maths for Computing
- Editor-in-Chief, IEEE Intelligent Informatics Bulletin, 2019-
- Managing Editor, Web Intelligence Journal, 2020-
- Associate Editor, Web Intelligence, an International Journal, 2015-
- Associate Editor, Scalable Information Systems, 2020-
- Guest Editor: Information Fusion, Elsevier, 2020
- Guest Editor: World Wide Web Journal, Springer, 2018, 2020
- Guest Editor, Multimedia Tools and Applications, Springer, 2018
- Guest Editor, Advances in Multimedia, Hindawi, 2018
- Guest Editor, International Journal of Machine Learning and Cybernetics, 2017
Regular Journal Reviewer (selected)
IEEE TKDE, TPDS, TCC, TSC, J-BHI, IEEE Intell. Syst., IEEE IoT, ACM TMIS, TWEB, TALIP, Neural Netw., Knowl.-Based Syst., INFFUS, Information & Management, PRL, Comput. Biol. Med., BMC Bioinformatics, WWWJ, HISS, SNAM, ECR, and more.
Conference Organisation (2014-)
- Program Committee Chair/Co-chair: WI 2017-18; BESC 2018, 2021, WI-IAT 2021
- Special Session Chair: BESC 2015, 2019
- Workshop Co-Chair: CPSCom 2015, APWeb-WAIM 2021
- Publication Co-chair: ASONAM 2014
- Session Chair: ADMA 2016; WI 2016, 2018, APWeb-WAIM 2021
- Workshop Organiser: KMWSM 2015, 2017-18; MLACS 2019
PC Member (selected, 2014-)
- 2021: IJCAI, PAKDD, WISE, ASONAM, APWeb-WAIM, DSAA
- 2020: PAKDD, WISE, ASONAM, APWeb, HIS, BESC, DSC, DSAA, AusDM
- 2019: PAKDD, ASONAM, HIS, BESC, AusDM, ICEBE
- 2018: PAKDD, ASONAM, HIS, BESC, AusDM, ICEBE, WI, ISMIS, APWeb, WISE
- 2017: PAKDD, ASONAM, HIS, BESC, AusDM, ICEBE, WI, ISMIS, IoP, ICA
- 2016: PAKDD, HIS, BESC, AusDM, ICEBE, WI, IoP, ICA, IMMM, BIH
- 2015: PAKDD, DSAA, BESC, AusDM, ICEBE, WI, IMMM, SocialCom
- 2014: PAKDD, ASONAM, BESC, AusDM, ICEBE, WI, IMMM, SocialCom, AMT