Special Session on "Statistical Learning for Data Science (SLDS)"
(an ASA sponsored conference)
Montreal, Canada October 17-19, 2016
Organized by
- 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)
Statistics plays a central role in the data science approach. This special session is to engage discussion from statisticians who study methods and theory that are fundamental to data science. Paper submissions on recent advances in statistical learning and modeling for complex data are encouraged.
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.
Journal publication
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.
Key dates
- Paper Submission deadline: Friday 20 May, 2016, 11:59 PM PDT
- Notification of acceptance: 15 July, 2016
- Final Camera-ready papers due: 19 August, 2016
Submission Instruction
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Tian Zheng
Associate Professor
Columbia University
http://www.stat.columbia.edu/~tzheng/
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