Guanjin (Brenda) Wang  from Murdoch University in Perth Australia.

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    Dr Guanjin (Brenda) Wang
    PhD (PolyU, UTS), MIT and BITS

    Senior Lecturer in Information Technology

    About me

    Guanjin (Brenda) Wang works as a senior lecturer in Information Technology. Before joining Murdoch University in November 2018, she obtained a joint Ph.D. degree from The Hong Kong Polytechnic University (PolyU) and University of Technology Sydney (UTS). It was a wonderful journey in which she became well prepared for being an independent researcher with a continuing scientific passion. Her current research focuses on artificial intelligence, machine learning, transfer learning, data mining and interdisciplinary solutions to real-world challenges. She has publications in top-tier journals and conferences, including IEEE TCYB, IEEE TFS, IEEE J-BHI, IEEE TSMC: Systems, IEEE TNSRE, Neurocomputing and IJCNN. She is currently a member of IEEE and ACS, Associate Fellow of AIDH and Fellow of HEA.

    Teaching area

    • ICT583 Data Science Applications
    • ICT606 Machine Learning
    Previous Teaching Units:
    • BSC301 Applied Research Skills in ICT
    • ICT521 IT Professional Practice
    • ICT602 Advanced Data Analysis
    I am also coordinating off-shore units at Murdoch Dubai and Singapore campuses.
    I am the second academic chair of Masters of IT.

    Research areas

    AI and Machine Learning

    • Learning from imperfect data, including imbalanced data problems, insufficient data problems, data heterogeneity problems, data uncertainty/imprecision problems, labeling problems, etc.
    • Model interpretability

    Interdisciplinary research, for example AI for Healthcare

    • The related project applications include bladder cancer prognosis, prostate cancer diagnosis, dementia risk detection, quality of life prediction, epilepsy detection, etc.

    Current projects

    Machine learning for disease risk analysis

    Machine learning for cancer detection and prognosis

    Machine learning for biosecurity-related issues

    Machine learning for social media analysis


    Professional and community service

    IEEE Member, ACS Member, Fellow of HEA, Associate Fellow of AIDH.

    Chairperson in IEEE Western Australia Section – SMC Chapter committee (2021-current)

    Vice Chairperson in IEEE Western Australia Section – SMC Chapter committee  (2019-2020)

    Treasurer in IEEE Western Australia Section – CIS/RAS Chapter committee (2018-current)

    Doctoral and masters supervisions

    PhD students (co-supervising):

    Bimal Philip Weerakody

    Lixia Ma



    • Wang, G., (2021), Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis, Journal of Medical Internet Research, 23, 5, .
    • Abdullat, A., Wang, G., Wang, Y., Lin, X., (2021), The dual concept of consumer value in social media brand community: A trust transfer perspective, International Journal of Information Management, 59, , pages -.
    • Thompson, N., Wang, G., Baskerville, R., (2021), Improving IS Practical Significance through Effect Size Measures, Journal of Computer Information Systems, , , pages -.
    • Niu, X., Wang, G., (2021), When I feel invaded, I will avoid it: The effect of advertising invasiveness on consumers avoidance of social media advertising, Journal of Retailing and Consumer Services, 58, , pages 102320 -.
    • Weerakody, B., Wong, K., Wang, G., Ela, W., (2021), A review of irregular time series data handling with gated recurrent neural networks, Neurocomputing: an international journal, 441, , pages 161 - 178.
    • Zhang, Y., Wang, G., Chung, F., Wang, S., (2021), Support vector machines with the known feature-evolution priors, Knowledge-Based Systems, 223, , pages 107048 -.
    • Wang, G., Zhang, G., Choi, K., Lam, K., Lu, J., (2020), Output based transfer learning with least squares support vector machine and its application in bladder cancer prognosis, Neurocomputing: an international journal, 387, 28 April 2020, pages 279 - 292.
    • Chen, L., Zhi, X., Wang, H., Wang, G., Zhou, Z., Yazdani, A., Zheng, X., (2020), Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique, Electronics, 9, 11, pages -.
    • Wang, G., Teoh, J., Lu, J., Choi, K., (2020), Least squares support vector machines with fast leave-one-out AUC optimization on imbalanced prostate cancer data, International Journal of Machine Learning and Cybernetics, Published 27 Feb 2020, , pages -.
    • Wang, G., Choi, K., Lu, J., (2020), A deep-ensemble-level-based interpretable Takagi-Sugeno-Kang fuzzy classifier for imbalanced data, IEEE Transactions on Cybernetics, , , .
    • Wang, G., Choi, K., (2019), Using Dual Neural Network Architecture to Detect the Risk of Dementia With Community Health Data: Algorithm Development and Validation Study, JMIR Medical Informatics, 8, 8, .

    The detailed publications can be found on Google Scholar