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Professor Yan Li

Professor Yan Li
Name Yan Li
Position Professor (Computing)
Section School of Sciences
Office D221
Location Toowoomba Campus
Phone +61 7 4631 5533
Extension 5533
Qualifications BEng HUST , MEng HUST , PhD Flinders

Prof Yan Li received her PhD degree from the Flinders University of South Australia, Australia. She is currently a full Professor in Computer Science and the Associate Head (Research) in the School of Sciences at the University of Southern Queensland (USQ), Australia. Her research interests lie in the areas of Artificial Intelligence, Machine Learning, Big Data and Internet Technologies, Signal/Image Processing and EEG Research, etc. Prof Yan Li has published more than 180 high quality publications, supervised dozens of PhD completions, and obtained more than $2.2 million research grants from Australia government and through international collaborations. Prof Yan Li is the leader of USQ Data Science Programs and the recipient of many research and teaching excellence awards, including 2012 Australia prestigious National Learning and Teaching Citation Award, 2008 Queensland Government Smart State-Smart Women Award, 2009 USQ Teaching Excellence Award, 2009 USQ Research Excellence Award, and 2015-2018 Research Publication Excellence Awards. Prof Yan Li has served as an elected academic leader in many high-level university committees, such as USQ Academic Board Executive Committee and USQ Research Committee etc.

Fields of Research (FoR)

  • Computer Communications Networks ( 100503 )
  • Signal Processing ( 090609 )
  • Biomedical Engineering ( 090300 )
  • Artificial Intelligence and Image Processing ( 080100 )

Research interests
Machine Learning Algorithms, Big Data Analytics, Signal/Image Processing, EEG Research, Graph Theory, and Networking Technologies.

Prof Yan Li has recently received more than half a million ($543,227) international industry research fund to refine her new cutting-edge technologies for identifying sleep patterns and stages for the diagnoses and treatment of sleep disorders, and for developing a better depth of anaesthesia monitoring device.

The projects require expertise from multi-disciplinary areas of artificial intelligence, brain modelling, signal processing, and big data analytics. Her current main is to transfer the knowledge to benefit business and industry.

Currently teaching courses/programs
CSC8003 Machine Learning
CSC8002 Big Data Management
CSC3407 Network Fundamentals and Routing
CSC3427 Switching, Wireless and WAN Technologies
CSC2407 Introduction to Software Engineering
Master of Science Research Projects A-F (SCI9011 - SCI9016)

Administrative responsibilities
• Associate Head (Research)
• USQ Cisco Networking Academy Founder and Leader
• Applied Data Science Program Leader
• Master of Science (Research) Program Leader

A core member of the following committees:
• Chair, School Research Committee
• School Executive Committee
• USQ Science Advisory Committee
• USQ Academic Board
• USQ Research and Research Training Committee
• USQ Research Committee
• USQ Academic Board Executive Committee

Research most recent
1. (Book) Siuly, Yan Li and Yanchun Zhang, Advanced techniques for electroencephalogram signal analysis and classification with their applications, Springer Singapore, January 2017. ISBN: 978-3-319-47652-0.

2. Meeras Salman Al-Shemarry, Yan Li, Shahab Abdulla and Peng Wen, A Multi-Level Extraction Method for Detecting Vehicles Licence Plates with Low Quality Images Based on Ensemble of Extreme Learning Machine, IEEE Transactions on Intelligent Transportation Systems, pp. 1-12, Vol 99, 2019, DOI: 10.1109/TITS.2019.2897990, Q1, (Impact Factor: 5.744, Q1)

3. Bo Song, Peng Wen, Yan Li and Tony Ahfock, Numeric Investigation of Brain Tumor Influence on the Current Distributions during Transcranial Direct Current Stimulation, IEEE Transactions on Biomedical Engineering, Vol.63, No. 1 January 2016, pp. 176-187. DOI: 10.1109/TBME.2015.2468672. (IF: 2.233, SNIP: 1.996)

Research most notable
1. Yan Li and A B Rad, A Cascading Structure and Training Method for Multilayer Neural Networks, International Journal of Neural Systems, Vol. 8, Nos. 5 & 6 (Oct. /Dec., 1997) pp. 509-515. (Impact factor: 6.056).

2. Tai Nguyen-Ky, Peng (Paul) Wen, and Yan Li. Consciousness and depth of anaesthesia assessment using Bayesian techniques. IEEE Transactions on Biomedical Engineering. Vol. 60(6), pp. 1488-1498, 2013. ISSN 0018-9294. (ERA A*, Impact Factor: 2.233).

3. Siuly and Yan Li, Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain computer interface, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 20, No. 4, July 2012, pp. 526-538. (ERA A*, Impact factor 2.821)

4. Guohun Zhu, Yan Li and Peng Wen, Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs from a Single Channel EEG Signal, IEEE Journal of Biomedical and Health Informatics, DOI: 10.1109/JBHI.2014.230399, 2014. (ERA Rank A*, Impact Factor: 1.98).

Publications in ePrints

Al-Shemarry, Meeras Salman ORCID: and Li, Yan (2020) Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates. IEEE Access, 8. pp. 170951-170966.

Diykh, Mohammed and Li, Yan and Abdulla, ShahabORCID: (2020) EEG sleep stages identification based on weighted undirected complex networks. Computer Methods and Programs in Biomedicine, 184 (Article 105116). pp. 1-14.

Al-Musaylh, Mohanad S. and Deo, Ravinesh C.ORCID: and Li, Yan (2020) Electrical energy demand forecasting model development and evaluation with maximum overlap discrete wavelet transform-online sequential extreme learning machines algorithms. Energies, 13 (9):2307.

Wan, Xiangkui and Zhu, Binru and Jin, Zhiyao and Zhang, Mingru and Li, Yan (2020) Multiscale entropy algorithms and their applications in cardiac disease discrimination. Journal of Mechanics in Medicine and Biology, 20 (8):2050052. pp. 1-13. ISSN 0219-5194

Al Ghayab, Hadi Ratham and Li, Yan and Siuly, S. and Abdulla, ShahabORCID: (2019) A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification. Journal of Neuroscience Methods, 312. pp. 43-52. ISSN 0165-0270

View full listing in ePrints