Call For Papers
(New deadline: June 12, 2016)
Special Session on
"Statistical Learning for Data Science (SLDS)"
Montreal, Canada October 17-19, 2016
- Tian Zheng (Department of Statistics, Columbia University)
- Wei Pan (Department of Biostatistics, University of Minnesota)
- Hernando Ombao, (Department of Statistics, University of California at Irvine)
Program Committee members
- Genevera Allen (Department of Statistics, Rice University)
- Ke Deng (Center for Statistical Science, Tsinghua University)
- Charles Doss (School of Statistics, University of Minnesota)
- Bailey Fosdick (Department of Statistics, Colorado State University)
- Xi Luo (Department of Biostatistics and Center for Statistical Sciences, Brown University)
- Ali Shojaie (Department of Biostatistics, University of Washington)
- Gongjun Xu (School of Statistics, University of Minnesota)
- Alexander Volfovsky (Department of Statistical Science, Duke University)
Topics of interests are, but not limited to,
- Advances in theory or models associated with the analysis of massive, complex datasets;
- Statistical modeling and data mining for data-driven solutions of real-world problems;
- Innovative data mining algorithms or novel statistical approaches;
- Comparison of techniques to solve a problem, along with an objective evaluation of the analyses and the solutions.
Conference content will be submitted for inclusion into IEEE Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top quality papers accepted and presented at the conference will be selected for extension and publication in the special issues of some international journals, including IEEE TKDE, ACM TKDD, ACM TIIS and WWWJ.
Extended versions of accepted papers to this special session will be considered for a special issue of Statistical Analysis and Data Mining, the ASA data science journal.
- Paper Submission deadline: Sunday 12 June, 2016, 11:59 PM PDT
- Notification of acceptance: 15 July, 2016
- Final Camera-ready papers due: 19 August, 2016