Senior Member of Institute of Electrical and Electronics Engineers, IEEE
Dr. Paul Pang obtained his Ph.D. in intelligent systems, from Shanghai Jiao Tong University, China (2000). In 2003, he completed his postdoctoral research at Pohang University of Science and Technology (POSTECH), South Korea. From July 2003 to March 2011, he was a senior research fellow and research centre director at Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand.
He is currently a senior lecturer in the Department of Computing, Unitec Institute of Technology, New Zealand.Research Interests
Dr Pang's research interests include Agent Data Mining, Incremental and Multi-task Learning, SVM Aggregating Intelligence, Intelligent Systems and Industry Applications.
He is a principal researcher in the Decentralized Machine Learning Intelligence Laboratory (DMLI).Teaching
- Data Mining and its applications to business and industry
- Research and professional practice at Doctorate Level
- Thesis supervision at masters and doctorate Level
Supervised thesis projects
Zhao, Jane (2016). Computer monitoring trends in recreational fishing effort
You, Li (2014). A multi-agent integrated analysis engine for decentralized network traffic monitoring
Song, Lei (2014). Spatio-temporal incremental data modelling for multidimensional environmental analysis
Martin, Bernd H. (2013). A methodology for multilevel analysis of scientific collaboration networks : mapping current computer science research in New Zealand
Tirumala, Sreenivas Sremath (2013). A quantum inspired competitive coevolution evolutionary algorithm
Veerisetty, Neeharika (2013). Load balancing in a distributed network environment : an ant colony inspired approach
Yip, Kai Leung (2012). Determining the accuracy of budgets : a machine learning application for budget change pattern recognition
Lei Zhu (2012). Dynamic class imbalance learning for incremental LPSVM
Zhang, Bing Qian (2011). An application of soft systems methodology on a holistic level: Recommendations for developing and implementing green ICT strategies in New Zealand
Zhao, J., Pang, S., Hartill, B., & Sarrafzadeh, A. (2015). Adaptive Background Modeling for Land and Water Composition Scenes. Murino, V., Puppo, E., & Vernazza, G. ICIAP2015.
Song, L., Pang, S., Longley, I., Olivares, G., and Sarrafzadeh, A. (2014). Spatio-temporal PM 2.5 prediction by spatial data aided incremental support vector regression. Neural Networks (IJCNN), 2014 International Joint Conference, XX.
Tirumala, S. S., Chen, G., & Pang, S. (2014). Quantum Inspired Evolutionary Algorithm by Representing Candidate Solution as Normal Distribution. Lecture Notes in Computer Science, Neural Information Processing (Vol. 8836).
Chen, G., Zhang, M., Pang, S., & Douch, C. (2014). Stochastic Decision Making in Learning Classifier Systems through a Natural Policy Gradient Method. Lecture Notes in Computer Science, Neural Information Processing (Vol. 8836).
Narayanan, A., Chen, Y., Pang, S., and Ban, T. (2012). The Effects of Different Representations on Malware Motif Identification. Proc. of Eighth International Conference on Computational Intelligence and Security (CIS).
Chen, Y., Narayanan, A., Pang, S., and Ban, T. (2012). Multiple sequence alignment and artificial neural networks for malicious software detection. Proc. of 2012 Eighth International Conference on Natural Computation (ICNC), Digital Object Identifier: 10.1109/ICNC.2012.6234576.
Pang, S., Liu, F., Kadobayashi, Y., Ban, T., and Inoue, D. (2012). Training Minimum Enclosing Balls for Cross Tasks Knowledge Transfer. International Conference on Neural Information Processing (ICONIP).
Chen , Y., Narayanan, A., Pang, S., and Ban, T. (2012). Malicioius Software Detection Using Multiple Sequence Alignment and Data Mining. The 26th IEEE International Conference on Advanced Information Networking and Applications (AINA).
Zhu, L., Pang, S., Chen, G., and Sarrafzadeh, A. (2012). Class Imbalance Robust Incremental LPSVM for Data Streams Learning. International Joint Conference on Neural Networks (IJCNN2012), Brisbane, Australia.
Lai, A., Song, L., Peng, Y., Zhang, P., Wang, Q., and Pang, S. (2012). Exploring Crude Oil Impacts to Oil Stocks through Graphical Computational Correlation Analysis. Lecture Notes in Computer Science (Vol. 7667).
S., Pang, T., Ban, Y., Kadobayashi, and N. K., Kasabov (2011). LDA Merging and Splitting With Applications to Multiagent Cooperative Learning and System Alteration. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on (Vol. 99).
Pang, S., Ban, T., Kadobayashi, Y., and Kasabov, N. (2011). LDA Merging and Splitting With Applications to Multiagent Cooperative Learning and System Alteration. IEEE Transactions on System, Man, and Cybernetics-Part B (Vol. PP/99).
Ozawa, S., Pang, S., and Kasabov, N. (2010). Online Feature Extraction for Evolving Intelligent Systems. P. Angelov, D. P. Filev and N. Kasabov (Eds). Evolving Intelligent Systems, John Wiley & Sons, Inc., Hoboken, JK, USA. doi: 10.1002/9780470569962.ch7.
Pang, S., Chen, G., Lei, S., and Kasabov, N. (2010). High Speed Algorithms for Outlier Detection and Classification over Huge-size Network Data Streams. Technical Project Report to National Institute of Information and Communication Technologies (NICT), Japan. Knowledge Engineering Discovery Research Institute, Auckland University of Technology.
Shimo, N., Pang, S., Horio, K., Kasabov, N., Tamukoh, H., Koga, T., Sonoh, S., Isogai, H., Yamakawa, T., Hanazawa, A., Miki, T., and Horio, K. (2010). Effective and Adaptive Learning Based on Diversive/Specific Curiosity. Brain-Inspired Information Technology, Isbn: 978-3-642-04024-5 (Vol. Volume 266).
Michlovsky, Z., Pang, S., Kasabov, N., Ban, T., and Kadobayashi, Y. (2009). String kernel based SVM for internet security implementation. NEURAL INFORMATION PROCESSING Lecture Notes in Computer Science (Vol. 5864/2009).
Pang, S., Dhoble , K., Chen, Y., Kasabov, N., Ban, T., and Kadobayashi, Y. (2009). Active Mode Incremental Nonparametric Discriminant Analysis Learning. Proc. of the Eighth International Conference on Information and Management Sciences.
Gordon, S., Pang, S., Nishioka, R., Kasabov, N., and Yamakawa, T. (2009). Vision based mobile robot for indoor environmental security. ADVANCES IN NEURO-INFORMATION PROCESSING Lecture Notes in Computer Science, DOI: 10.1007/978-3-642-02490-0_117 (Vol. 5506/2009).
Pang, S., Havukkala, I., Hu, Y., and Kasabov, N. (2008). Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems. In Y.-Q. Zhang and J. C. Rajapakse (Eds.). Machine Learning in Bioinformatics, Hoboken, NJ: John Wiley & Sons.
Pang, S., and Kasabov, N. (2008). Wireless Network Coverage and Capacity Dynamic Optimization for NewZealand Telecom. Technical Project Report to Telecom New Zealand Ltd., Lucentand Bell Lab, and Foundation for Research Science & Technology (FRST) NewZealand, Knowledge Engineering Discovery Research Institute, Auckland Universityof Technology.
Shimo, N., Pang, S., Kasabov, N., and Yamakawa, T. (2008). Curiosity-Driven Multi-Agent Competitive and Cooperative LDA Learning. International Journal of Innovative Computing, Information and Control (Vol. 4/7).
Pang, S., Ban, T., Kadobayashi, Y., and Kasabov, N. (2008). gSVMT: Aggregating SVMs over a Dynamic Grid Learned from Data. Proc. of 11th International Conference on Computer and Information Technology.
Pang, S., and Chen, G. (2007). Speech Feature Extraction with reference to Smoking Quitting Voice Change Detection. Technical Project Report to Health Research Council (HRC) New Zealand, Knowledge Engineering Discovery Research Institute, Auckland University of Technology.
Pang, S., Havukkala , I., and Kasabov, N. (2006). Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis. In J. Wang et al. (Eds.). ISNN 2006: Proceedings of International Symposium on Neural Networks, Berlin: Springer Verlag.
Pang, S., and Havukkala, I. (2006). Proceeding of 1st Korean New Zealand Joint Workshop on Advance of Computational Intelligence Methods and Applications. Auckland University of Technology, New Zealand.
Ozawa, S., Pang, S., and Kasabov, N. (2006). Incremental Learning of Feature Space and Classifier for Online Pattern Recognition. Computer Science, Artificial Intelligence and Software Development (Vol. 10/1).
Ozawa, S., Pang, S., and Kasabov, N. (2006). On-line Feature Selection for Adaptive Evolving Connectionist Systems. International Journal of Innovative Computing, Information and Control (Vol. 10/10).
Havukkala, I., Benuskova, L., Pang, S., Jain, V., Kroon , R., and Kasabov, N. (2006). Image and Fractal Information Processing for Large-Scale Chemoinformatics, Genomics Analyses and Pattern Discovery. Proceedings of PRIB.
Pang, S., and Kasabov, N. (2006). Investigating LLE Eigenface on Pose and Face Identification. ADVANCES IN NEURAL NETWORKS - ISNN 2006 Lecture Notes in Computer Science, DOI: 10.1007/11760023_21 (Vol. 3972/2006).
Ozawa, S., Toh, S.L., Abe, S., Pang, Shaoning, and Kasabov, N. (2005). Incremental learning for online face recognition. Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on (Vol. 5/5).
Pang, Shaoning, Ozawa, S., and Kasabov, N. (2005). Incremental linear discriminant analysis for classification of data streams. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on (Vol. 35/5).
Kasabov, N., Zhang, D., and Pang, S. (2005). Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition. Advanced Robotics and its Social Impacts, 2005. IEEE Workshop on.
Kasabov, N., and Pang, Shaoning (2003). Transductive support vector machines and applications in bioinformatics for promoter recognition. Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on (Vol. 1).