A Letter from Dr. Xiaotong Shen, President of the Section on SLDM

September 14, 2014

Dear Friends of the Section on Statistical Learning and Data Mining,

I am pleased to report that we made a lot of progress on various research and educational issues at the business meeting of the Section during JSM 2014 in Boston on Tuesday, August 5, 2014. Below is a summary of the key outcomes of the meeting:

1.      Special Issue of Statistical Analysis and Data Mining: The ASA Data Science Journal: Journal editor David Madigan of Columbia University will run a special JSM 2014 issue, with papers selected from invited or organized sessions. Section President Xiaotong Shen has posted a request for papers to the Section’s mailing list on 8/9/2014 soliciting papers for the special issue, with a submission deadline of 12/31/2014.

2.      Donations: Pandora has kindly donated $3,000 to the Section, of which $1,000 was awarded in aggregate to the 2014 student paper winners at JSM 2014. We are very grateful to Pandora for their sponsorship of our Section.

3.      Leo Breiman Award: The Section made a lot of progress in creating operating procedures for a proposed Leo Breiman Award for excellence in research, to be awarded at the Section’s reception at the JSM every year. More details on this award and eligibility are forthcoming.

4.      2016 SLDM Conference: The Section is finalizing the location of the 2016 SLDM conference and will provide details shortly.

5.      Data Competitions: The Section is in early discussions with a private company and with another Section of the American Statistical Association to explore collaborative opportunities to conduct data mining competitions in the coming years. More information on this front as soon as the details are finalized.

As always, please feel free to get in touch with me or with any of the Section Officers if you have any questions or comments. We value your feedback and look forward to hearing from you.


Xiaotong Shen
University of Minnesota
President, Section on Statistical Learning and Data Mining