School of Computing & Mathematics

Junbin Gao graduated from Huazhong University of Science and Technology  (HUST), China  in 1982 with B.Sc. degree in Computational Mathematics and obtained PhD from  Dalian University of Technology, China in 1991. In July of 2005, he joined the  School of Information Technology (now Computing and Mathematics) at Charles Sturt  University, Australia, as an Associate Professor in Computing Science. He was a  senior lecturer, a lecturer in Computer Science from 2001 to 2005 at University  of New England, Australia. From 1982 to 2001 he was an associate lecturer,  lecturer, associate professor and professor in Department of Mathematics at  HUST. His main research interests include machine learning, kernel method,  Bayesian learning and inference, and image processing.  Dr. Gao has published more than 120 papers  and two books on machine learning, pattern recognition and Bayesian inference.

Teaching Responsibilities

• ITC230 Introduction to Web Development
• ITC322 Data Structure
• ITC364 Computational Intelligence
• ITC368 Image Processing and Analysis
• ITC527 Concurrent Programming
• MTH129  Discrete Mathematics

Administrative Responsiblities

• Computing Discipline Leader
• Chair of School Research Committee
  
• Member of the Faculty Research Committee
• Member  of the Faculty Board

Research

Research Focus

• Neural Network and Neural Computation
• Machine Learning and Artificial Intelligence
  
• Statistical Modelling (Nonparametric and Bayesian Methods)
• Data Mining with Machine Learning Methods
• Image Processing and Object Recognition
• Dimensionality  Reduction

Research Project Supervision

• Machine Learning
• Statistical Learning
• Pattern Recognition
• Classification
• Data Mining
• Image Processing
• Computer Visualization

Current Research Students

• PhD Students
  Mr. Lennon Cook
  Mr. Adrian Letchford
  Mr. Geoff Fellows
  Mr. Hürol Türen
• DIT Students
  Mr. Andrew Phipps
  Mr. Campbell Gunn
• Master Student (Research)
  Mr. Adrian O'Connor
  

Professional Activities

Journal Reviewer for

• IEEE Transactions on Neural Networks
• IEEE Transactions on System,  Man and Cybernetics, Part A and Part B
• Neurocomputing
• Knowledge-based Systems
• Journal of Machine Learning  Research
• Journal of Knowledge and  Information Systems
• Pattern Recognition Letters
• International Journal of  Computer Vision
• International Journal of  Systems Science
  

Grants and Awards

• Non-vectorial kernel machine and its application in active shape modelling, supported bythe National Natural Science Foundation of China, Grant no. 60373090,2004-2006, Chinese RMB 220,000.
• NewKernel Approaches to Gene Function Prediction, supported by the research grant of the University of New England, Australia, 2005, $10,000
• Investigationinto Shape Kernels, ARC Project Development Grant of Charles Sturt University, Australia,2005, $4,000
• Investigation of Imaging Techniques to Determine Muck Pile Ore Fragment Size In-Situ, Newcrest Mine Australia, 2008-2010, $910,000

Selected Publications

Books

• F.T. Chau, Y.Z Liang, J. Gao and S. Zhao, Chemometrics: From Basic to Wavelet Transform, Vol.164 Chemical Analysis Series, by John Wiley & Sons, ISBN 0-471-20242-8, 2004.
• Kok-Leong Ong, Wenyuan Li and Junbin Gao, The proceedings of the 2nd International Workshop on Integrating AI and Data Mining (AIDM 2007), Gold Coast, Australia. December 2007, ISBN 978-1-920682-65-1, ISSN 1445-1336 (Vol. 84).

Book Chapter

• Gao,Junbin and Lei Zhang, The Error Bar Estimation for the Soft Classification with Gaussian Process Models, in Applied Soft Computing Technologies: The Challenge of Complexity, series of Advances in Soft Computing, Vol.XXXIII (2006), editors: Abraham, A.; Baets, B.; Koeppen, M. and Nickolay,B., pp669-677. ISSN: 1615-3871

Refereed Journal Articles

• P Kwan, K. Kameyama, Junbin Gao and K. Toraichi, Content-based Image Retrieval of Cultural Heritages Symbols by Interaction of Visual Perspectives, accepted by International Journal of Pattern Recognition and Artificial Intelligence
• Junbin Gao, J. Zhang and D. Tien, Relevance Units Latent Variable Model and Nonlinear Dimensionality Reduction, IEEE Transactions on Neural Networks, Vol. 21:1 (2010), pp. 123-135
• Junbin Gao, P. Kwan and D. Shi, Sparse Kernel Learning with LASSO and Its Bayesian Inference, Neural Networks, Vol. 23 (2010), Issue 2, pp 257-264
• P. Kwan, Junbin Gao, Y. Guo and K. Kameyama, A Learning Framework for Adaptive Fingerprint Identification, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 24:1 (2010), pp15 - 38.
• Junbin Gao, P. Kwan and X. Huang, Comprehensive Analysis for the Local Fisher Discriminant Analysis, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 23 (2009), 1129-1143.
• Junbin Gao, P. Kwan and Y. Guo, Robust Multivariate L1 Principal Component Analysis and Dimensionality Reduction, Neurocomputing, Vol. 72 (2009), pp 1242-1249.
• Y. Guo, Junbin Gao and P. Kwan, Twin Kernel Embedding, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30 (2008), pp 1490-1495.
• Y. Guo, Junbin Gao, P. Kwan and K.X. Hou, Visualization of Protein Structure Relationships Using Constrained Twin Kernel Embedding, Journal of Biomedical Science and Engineering, Vol. 1 (2008), pp 133-140.
• Junbin Gao, Robust L1 Principal Component Analysis and Its Bayesian Variational Inference, Neural Computation, Vol.20:2 (2008), pp555-572.
• J. Gao, D.M. Shi and X.M. Liu, Critical Vector Learning to Construct Sparse Kernel Regression Modelling, Neural Networks, Vol 20 (2007), No. 7, pp 791-798.
• J. Gao, Robust L1 Principal Component Analysis and Its Bayesian Variational Inference, Neural Computation, Vol.20:2 (2008), pp555-572.
• T. Tian, S. Xu, J. Gao and K. Burrage, Simulated maximum likelihood method for estimating kinetic rates in gene expression, Bioinformatics, Vol.23 (2007), pp 84-91.
• D. Shi, D.S. Yeung and J. Gao, Sensitivity analysis applied to the construction of radial basis function networks, Neural Networks, Vol. 18(2005), p951-957.
• J. Gao, S.R. Gunn and C.J. Harris, Mean Field Method for the SVM Regression, Neurocomputing, Vol. 50 (2003), 391-405.
• J. Gao, S.R. Gunn and C.J. Harris, SVM Regression through Variational Methods and Its Sequential Implementation, Neurocomputing, Vol.55 (2003), pp151-167.
• J. Gao, S.R. Gunn, C.J. Harris and M.Q. Brown, A Probabilistic Framework for SVM Regression and Error Bar Estimation, Machine Learning, Vol.46 (2002), pp 71-89.
• J. Gao and C.J. Harris), Adaptive Multiscale Basis Method for Modelling Discretetime Nonlinear Dynamic Systems, International Journal of Control, Vol.75, No.3 (2002), pp 141-153.
• J. Gao and C.J. Harris, Some Remarks on Kalman Filters for the Multisensor Fusion, Information Fusion Journal, Vol.3 (2002), 191-201.
• J. Gao, C.J. Harris and S.R. Gunn, On a Class of Support Vector Kernels Based on Frames in Function Hilbert Spaces, Neural Computation, Vol.13, No.9 (2001), pp 1975-1994.

Refereed Conference Proceedings (2009 Onwards)

• Martin McGrane, Simon Poon, Josiah Poon, Xuezhong Zhou, Runshun Zhang, Baoyan Liu, Clement Loy, Paul Kwan, Kelvin Chan, Daniel Sze and Junbin Gao, Analysis of Synergistic and Antagonistic Effects of TCM: Cases on Diabetes and Insomnia, The International Workshop on Information Technology for Chinese Medicine (ITCM2010), pp. XXX. Hongkong, Dec. 18-21, 2010 68.
• Josiah Poon, Simon Poon, Xuezhong Zhou, Runshun Zhang, Dawei Yin, Baoyan Liu, Clement Loy, Paul Kwan, Kelvin Chan, Daniel Sze, and Junbin Gao, Using Complementarity to Study HerbHerb Interaction The International Workshop on Information Technology for Chinese Medicine (ITCM2010), pp. XXX. Hongkong, Dec. 18-21, 2010
• Lennon Cook and Junbin Gao, Dimensionality reduction for classification through visualisation using L1SNE, J. Li and J. Debenham (Eds.): AI 2010, Lecture Notes on Artificial Intelligence, Vol. 6464 (2010), pp. 204-212. Springer, Heidelberg
• X. Jiang, Junbin Gao, T.Wang and P. Kwan, Learning Gradient via Gaussian Process, M.J. Zaki et al. (Eds.): PAKDD 2010 (acceptance rate 10.1%), Part II, Lecture Notes on Artificial Intelligence, Vol. 6119, pp. 113-124, 2010.
• Allen Benter, Richard Xu, Wayne Moore, Michael Antolovich and Junbin Gao, Fragment size detection within homogeneous material using Ground Penetrating Radar, Radar Conference – Surveillance for a Safer World (RADAR), pp. 1-5, 2009, ISBN 978-2-912328-55-7.
• Yi Guo, Junbin Gao and Paul W. Kwan, Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data, A. Nicholson and X. Li (Eds.): AI 2009, Lecture Notes on Artificial Intelligence, Vol. 5866 (2009), pp. 240-249. Springer, Heidelberg
• J. Zhang, J. Gao, and J. Tian, Relevance units machine based on akaike’s information criterion, in Proceedings of the Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, ser. Pattern Recognition and Computer Vision, M. Ding, B. Bhanu, F. Wahl, and J. Roberts, Eds., vol. 7496. Yichang, China: SPIE, 2009, pp. 1-8.
• A. O’Connor, Junbin Gao and J. Louis, Termination Criteria for Evolutionary Algorithms, Proceeding of the 2009 International Conference on Genetic and Evolutionary Methods (GEM’09), CSREA Press 2009, pp 35-42.
• A. O’Connor, Junbin Gao and J. Louis, Initiation of Evolutionary Algorithms, Proceeding of the 2009 International Conference on Genetic and Evolutionary Methods (GEM’09), CSREA Press 2009, pp 73-78.
• P. Kwan, Junbin Gao and Graham Leedham, A User-Centered Framework for Adaptive Fingerprint Identification, Lecture Notes on Computer Science, Vol.5477(2009), pp89-100, H. Chen et al. (Eds.): Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2009) joint with the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-09).
• Junbin Gao and Jun Zhang, Sparse Kernel Learning and the Relevance Units Machine, Lecture Notes on Computer Science, Vol.5476(2009), pp612-619, T. Theeramunkong et al. (Eds.): Proceedings of The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-09).