报告时间：2016年11月3日（周四）下午2:30

报告地点： 教二215

报 告 人： Houping Xiao is a Ph.D. candidate in the Department of Computer Science and Engineering at SUNY Buffalo. His research interests lie broadly in data mining and machine learning, including truth discovery, multi-source information trustworthiness analysis, tensor decomposition, etc .Before joining SUNY Buffalo in 2013, he got B.S. degree in Statistics from Beijing Normal University in 2011. After graduation, He worked as a research assistant in City University of Hong Kong, Supervised by professor Ding-Xuan Zhou on learning theory.

报告题目：Towards Truth Discovery in Big Data

报告摘要：In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources, where a data source can be a web page, a database or a person. Consequently, conflicts among sources become inevitable. To resolve the conflicts and achieve high quality data, truth discovery has been studied intensively. In this talk, I will first present an organized picture on truth discovery, then focus on one important component of truth discovery, how to qualify the confidence on the estimated truths.