Xizhao Wang

Xizhao Wang

  • Professor
  • Research Institute of Big Data Technology and Application
  • #619


Brief CV

Wang Xizhao, Ph.D. (1998), Professor (1998), IEEE Fellow (2012), CAAI Fellow (2017), Editor-in-Chief of Springer Journal Machine Learning and Cybernetics (2010), Deputy chair of CAAI machine learning committee (2012), and knowledge engineering committee (2013), overseas high-level (peacock B class) talent of Shenzhen city (2015), prizewinner of First-Class Award of Natural Science Advances of Hebei Province (2007), and Model Teacher of China (2009).

Prof. Wang received his doctor degree in computer science from Harbin Institute of Technology in September 1998. From 1998 to 2001 Prof. Wang worked at Department of Computing in Hong Kong Polytechnic University as a research fellow. From October 2000 to March 2014 Prof. Wang served in Hebei University as the Dean of school of Mathematics and Computer Sciences. From October 2007 to March 2014 Prof. Wang was the founding Director of Key Lab. on Machine Learning and Computational Intelligence in Hebei Province, China. From September to November in 2013, Prof. Wang was a visiting professor of Canada Simon Fraser University. From December 2013 to January 2014, Prof. Wang was a visiting professor of University of Alberta in Canada. From July to September in 2014, Prof. Wang was a visiting professor of Australia New South Wales University at Canberra. Since March 2014 to now Prof. Wang has moved to college of computer science and software engineering in Shenzhen University as a professor and a director of Big Data Institute.

Prof. Wang's main research interest is machine learning and uncertainty information processing including inductive learning with fuzzy representation, approximate reasoning and expert systems, neural networks and their sensitivity analysis, statistical learning theory, fuzzy measures and fuzzy integrals, random weight network, and the recent topic: machine learning theories and methodologies in Big-Data environment. The main research feature is, through discovering and representing the uncertainty hidden in big data, to dig the distribution of big data and then use distributed parallel technology to design and implement classification and clustering algorithms which are suitable for different types of big data. It focuses on the corresponding key issues of theory and technology of big data analytics.

Academic contributions: (1) Putting forward the concept of "fuzzy learning from examples" for the first time in 1996 during his PhD thesis, and extending machine learning approaches into the uncertainty framework. His research in this aspect lasted almost 20 years and acquired a series of achievements with significant impact, for example, the project “fuzzy-valued attribute feature subset selection” won the first prize of Hebei province natural science in 2007. (2) Establishing a refinement methodology and technique for similarity based clustering, called departure-0,5, and extending it to a new branch of semi-supervised learning based on departure-0.5, and further applying successfully to the big data learning. Mainly due to this contribution Prof. Wang was elected as an IEEE Fellow in 2012. (3). Proposing the viewpoint that uncertainty modeling and its effective handling play a crucial/indispensible role in improving the generalization ability for a big data learning system. The view point is highly recognized by the experts in related domains, and is funded by a NSFC key project (Uncertainty modeling in learning from big data, 2018-2022).

Research achievements: Prof. Wang has published 3 monographs and 2 textbooks. He has also published 200+ research papers in famous magazine and conferences in the field of machine learning and uncertainty, among which 150+ publications have been included in SCI or EI databases. The journals include IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, Machine Learning, Information Sciences and Fuzzy Sets and Systems. By Google scholar in November 2017, the total number of citations is 6360, the maximum number of citations for a single paper is 600, and the SCI-H index is 42. Prof. Wang has completed 30+ research projects including ones funded by National Natural Sciences Fund of China, by Ministry of Education, by National Development and Reform Commission, by Hebei Province Natural Science, and by RGC in Hong Kong.

Awards and honors: Prof. Wang has received the First-Class Award of Natural Science Advances of Hebei Province and the Second-Class Award of Natural Science of Education Ministry in 2007. He was selected as one member of the first hundred of excellent innovative talents of Hebei province in 2007. He gained the honor of Model Teacher of China in 2009. Prof. Wang was evaluated as an IEEE Fellow in 2012 and a CAAI Fellow in 2017. He was chosen as the local leading talent of Shenzhen in 2013 and one of the Chinese scholars whose academic papers have been highly cited based on Elsevier statistics in 2014/15/16. Prof. Wang was identified as the overseas high-level (peacock B class) talent of Shenzhen in 2015..

last updated:2019/07/01

College of Computer Science and Software Engineering, Shenzhen University 2009-2016