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Associate Professor (Computing) Computing Discipline Team Leader School of Mathematics, Physics, and Computing University of Southern Queensland, Australia Tel: +61 7 4631 1576 Email: xiaohui DOT tao AT usq DOT edu DOT au http://www.tao-xiaohui.info https://staffprofile.usq.edu.au/Profile/Xiaohui-Tao |
Dr. Xiaohui Tao is an elected Senior Member of IEEE and ACM, an active researcher in AI, and Associate Professor (Computing) in School of Mathematics, Physics and Computing (SoMPC), 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 personalised 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 personalised 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., TKDE, IPM, KBS, ESWA, and PRL) and conferences (e.g., IJCAI, ICDE, CIKM, PAKDD and WISE). A/Prof. Tao received ARC DP grant (Ref. DP220101360) in 2022-24, Australian Endeavour Research Fellow in 2015-16, and was awarded with "Research Performance Award" and "Discipline Research Performance Improvement Award" by SoMPC, USQ, 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.
A/Prof. Tao has been active in professional services. He has served PC Chair in WI '17, '18, WI-IAT '21 and BESC '18 and '21. He has been an editor or guest editor in many journals including INFFUS and WWWJ, and also been a regular reviewer in many top-ranking journals such as TKDE, TPDS, and KBS.
Dr. Tao has also been actively participating in tertiary education ever since 2005 and taught a variety of IT/IS subjects. Currently, he is the Discipline Team Leader of Computing in SoMPC and Lead of the Intelligent Data Engineering and Analytics Group in School of Mathematics, Physics and Computing, USQ and is the Principal Supervisor of a number of PhD and Research Master students.
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Privacy Preservation over 5G and IoT Smart Devices This project aims to investigate privacy preservation protocols in a 5G integrated IoT environment through an analysis of the depth of smart-device use in common smart domains. 5G’s addition to IoT-based smart devices will be effectively deployed and utilised by a large majority of individual and organisation-based users. The knowledge-based ontology and tools developed in the project will help form the new privacy preservation mechanisms that are required for the 5G enabled environment. The construction of new AI-based tools and testing facilities as well as the generation of new knowledge in the field of privacy preservation and collaboration between universities are expected outcomes of this project.
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).