Recent Advances in Applied Matrix Technologies
Fei Wang, IBM T. J. Watson Research Center, USA
Hanghang Tong, IBM T. J. Watson Research Center, USA
Matrix is a natural representation for many real world data, such as an image, a collection of documents and an adjacency graph, and matrix related technologies has been very popular in data analytics research because of its nice interpretability, effectiveness and efficiency. This tutorial will overview the recent advances in applied matrix technologies, include nonnegative matrix factorization, rank minimization, sparse learning and online/distributed learning strategies. We will also present how these technologies are applied in social and healthcare informatics.
Fei Wang is currently a Research Staff Member in healthcare analytics research group, IBM T. J. Watson Research Center. He got his M.S. and Ph. D. degrees from Department of Automation, Tsinghua University in 2008. After that, he spent one year in School of Computing and Information Science, Florida International University as a posdoc and another year in Department of Statistical Science, Cornell University as a postdoc. His research interests include semi-supervised learning, clustering, relational learning, optimization, social network analysis and healthcare data analytics. He has published over 100 papers on the leading conferences like SIGKDD, SIGIR, ICML, IJCAI, AAAI, SDM, ICDM. He also serves as a refree for many distinguished journals including IEEE TPAMI, IEEE TKDE, DMKD, ACM TKDD and program committee member for many international conferences including ICDM and SDM. For more details, one can refer to his personal homepage at http://researcher.watson.ibm.com/researcher/view.php?person=us-fwang
Hanghang Tong is currently a research staff member at IBM T. J.Watson Research Center. Before that, he was a Post-doctoral fellow in Carnegie Mellon University. He received his M.Sc and Ph.D. degree from Carnegie Mellon University in 2008 and 2010, both majored in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has been a co-PI or PI in several projects on large scale social networks analysis sponsored by NSF, DARPA and Army Research Lab. He has received several awards, including best research paper in ICDM 2006 and best paper award in SDM 2008. He has published over 60 referred articles and served as a program committee of SIGKDD, PKDD, and WWW, etc. For more details, refer to his homepage at http://www.cs.cmu.edu/~htong.