Title Abstract:
Online service providers, such as Twitter, Amazon, Google, and Wikipedia, generate huge volumes of user behavior data daily, in which valuable patterns and correlations of user behaviors are hidden. For companies, effective analysis of the behavior data allows them to learn more about their customers on an unprecedented scale to improve customer relations and develop social media marketing strategies. For governments, effective tracking of the behavior data allows them to detect and predict critical events to make proper decisions in a timely manner. However, analysis of the behavior data is challenging due to the enormous amount of data and the heterogeneity of information. In my talk, I will discuss the challenges of the research on visual behavior analytics, and then give some examples of applying interactive visualization techniques to making sense of the behavior data.
Speaker's Bio:
Yingcai Wu is an assistant professor at the State Key Lab of CAD & CG, Zhejiang University. His main research interests are in visual analytics, information visualization, and human computer interaction, with focuses on urban computing, social media analysis, text visualization, and behavior analysis. He has co-authored more than 40 refereed papers including 17 papers in the IEEE Transactions on Visualization and Computer Graphics. His papers appear in almost all major visualization conferences, such as IEEE SciVis, IEEE InfoVis, IEEE VAST, EuroVis, and PacificVis. His papers have been awarded Honorable Mention at IEEE SciVis 2009, IEEE VAST 2014, and IEEE PacificVis 2016. He obtained a B.Eng. in Computer Science from the South China University of Technology, and a PhD (2010) in Computer Science from the Hong Kong University of Science and Technology. Prior to his current position, Yingcai Wu was a researcher in Microsoft Research Asia, Beijing, China from May 2012 to January 2015, and a postdoctoral researcher in the University of California, Davis from May 2010 to March 2012.