数学学科学术报告四：Big Data Learning with Uncertainty
报告题目：Big Data Learning with Uncertainty
报告人：王熙照 （深圳大学 教授）
Abstract: Big data refers to the datasets that are so large that conventional database management and data analysis tools are insufficient to work with them. Big data has become a bigger-than-ever problem with the quick developments of data collection and storage technologies. Model simplification is one of the most popular approaches to big data processing. After a brief tutorial of the existing techniques of processing big data, this talk will present some key issues of learning from big data with uncertainty, focusing on the impact of handling uncertainty and the challenges uncertainty brings to big data learning. It shows that the representation, measure, and handling of the uncertainty have a significant influence on the performance of learning from big data. Some new advances in our Big Data Institute regarding the research on big data analysis and its applications to different domains are briefly introduced.
Biography: Prof. Wang’s major research interests include uncertainty modeling and machine learning for big data. Prof. Wang has edited 10+ special issues and published 3 monographs, 2 textbooks, and 200+ peer-reviewed research papers. By the Google scholar, the total number of citations is over 5000 and the maximum number of citation for a single paper is over 200. Prof. Wang is on the list of Elsevier 2014/15/16/17 most cited Chinese authors. As a Principle Investigator (PI) or co-PI, Prof. Wang's has completed 30+ research projects. Prof. Wang is an IEEE Fellow, the previous BoG member of IEEE SMC society, the chair of IEEE SMC Technical Committee on Computational Intelligence, and the Chief Editor of Machine Learning and Cybernetics Journal.