Fusion of Multiple Heterogeneous Social Networks for Synergistic Knowledge Discovery
Speaker: Dr. Jiawei Zhang
University of Illinois at Chicago
February 14, 2017, 4:00 pm
Location: Troy 2018
Hosted By: Prof. Boleslaw Szymanski (x6838)
Whether the people we follow in Twitter can be recommended as our potential friends in Facebook? How is the box office that US movies can achieve in China? How do weather and nearby points-of-interest (POIs) affect the traffic routes planned for vehicles? About the same information entities, a large amount of information can be collected from various sources, each of which provides a specific signature of the entity from a unique underlying aspect. Effective fusion of these different information sources provides an opportunity for understanding the information entities more comprehensively.
My thesis works investigate the principles, methodologies and algorithms for knowledge discovery across multiple aligned information sources, and evaluate the corresponding benefits. Fusing and mining multiple information sources of large Volumes and diverse Varieties is a fundamental problem in Big Data studies. In this talk, I will discuss about the information fusion and synergistic knowledge discovery works, focusing on online social media, and present my algorithmic works on multi-source learning frameworks together with the evaluation results. I will also provide my future vision on fusion learning for broader real-world applications at the conclusion of the talk.
Jiawei Zhang is a Ph.D. candidate at the Depart of Computer Science at University of Illinois at Chicago (UIC), under the supervision of Prof. Philip S. Yu since August 2012. Prior to joining UIC, he obtained his Bachelor’s Degree in Computer Science from Nanjing University, in China. His research interests span the fields of Data Science, Data Mining, Network Mining, and Machine Learning. He research works focus on fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic.
His fusion learning works have appeared in KDD, ICDM, SDM, ECML/PKDD, IJCAI, WWW, WSDM, CIKM, IEEE Transactions on Knowledge and Data Engineering (TKDE). He receives the Best Student Paper Runner Up Award from ASONAM’ 16. He has been serving as the information specialist and director of ACM Transaction on Knowledge Discovery from Data (TKDD) since August 2014. He is also the PC member of WWW’ 17, KDD’ 16, CIKM’ 16, CIKM’ 15 and AIRS’ 16. Besides the academic experience at University of Illinois at Chicago, he also has industrial research experiences working at Microsoft Research in 2014, IBM T. J. Watson Research Center in 2015
Last updated: January 31, 2017