| A/Prof Junbin GAO | |
Junbin Gao joined Charles Sturt University as an Associate Professor in Computer Science in the School of Information Technology in July 2005. Before that he was a Senior Lectuter (Jan. 2005 - July 2005) / Lecturer (Nov. 2001 - Dec. 2004) in Computer Science in The School of Mathematics, Statistics and Computer Science at the University of New England (UNE), where he was the leader of "Computational Intelligence and Applications Group". Before he came to Australia, he worked as a senior research fellow in ISIS group of the University of Southampton from 1999 to 2001.
Junbin Gao graduated from Huazhong University of Science and Technology (HUST) in 1982 with a first class honours degree in Computational Mathematics. He obtained his PhD from Dalian University of Technology in 1991. Between 1991 and 1993 he worked as a postdoctoral research fellow investigating wavelet applications at Wuhan University. He was appointed as an Associate Professor in July 1993 and promoted to Professor in October 1997 in the Department of Mathematics of HUST, and he has been a council member of the Chinese Computational Mathematical Society (CCMS) since 1999. Now he is a Guest Professor in the State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing at Wuhan University, China.His research covers machine learning, neural networks, intelligent computation, wavelets and numerical analysis etc. He is the author of 65 academic research papers and two books.
His recent research has involved the development of new machine learning algorithms based upon kernel methods and their application to a variety of domains. Kernel methods are rapidly replacing neural networks as the preferred tool for machine learning due to many attractive features: a strong basis from statistical learning theory; no computational penalty in moving from linear to non-linear models; the resulting optimisation problem is convex, guaranteeing a unique global solution and consequently producing systems with excellent generalisation performance, i.e. they learn extremely well.
If you would like to do a PhD degree or collaborate with me, please contact me. A list of possible PhD titles/themes is available here.
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