SDM14: Call for Tutorials

Accepted Tutorials

Leveraging Social Media and Web of Data to Assist Crisis Response Coordination - PDF [3MB]
Carlos Castillo, Qatar Computing Research Institute, Qatar
Fernando Diaz, Microsoft Research, NYC, USA
Hemant Purohit, Kno.e.sis

Safer Data Mining: A Tutorial on Algorithmic Techniques in Differential Privacy - PDF [3MB]
Moritz Hardt, IBM Research, Almaden, San Jose, USA
Alexander Nikolov, Rutgers University, USA

Stochastic Optimization for Analyzing and Mining Big Data - PDF [2MB]
Tianbao Yang, NEC Laboratories America, Inc., USA
Rong Jin, Michigan State University, USA
Shenghuo Zhu, NEC Laboratories America, Inc., USA

Data Mining in Drug Discovery and Development - PDF [8MB]
Ping Zhang, IBM T.J. Watson Research Center, USA
Lun Yang, GlaxoSmithKline, USA

Node similarity, Graph Similarity and Matching: Theory and Applications - PDF [100KB]
Tina Eliassi-Rad, Rutgers University, USA
Christos Faloutsos, Carnegie-Mellon University, USA
Danai Koutra, Carnegie-Mellon University, USA

 

The SIAM Data Mining (SDM14) Organizing Committee invites proposals for tutorials to be held in conjunction with the conference. Tutorials are an effective way to educate and/or provide the necessary background to the intended audience enabling them to understand technical advances.

For SDM14, we are seeking proposals for tutorials on all topics related to data mining. A tutorial may be a theme-oriented comprehensive survey, discuss novel data mining techniques or may center around successful and timely application of data mining in important application areas (e.g. medicine, national security, scientific data analysis). For examples of typical SIAM tutorials, see the set of accepted tutorials at previous SIAM conferences SDM11, SDM12, and SDM13.

Tutorials are open to all conference attendees without any extra fees. The typical tutorial will be 2 hours long (longer tutorials will be considered). Previous SDM conferences attracted up to 100 attendees in a tutorial.
Proposals should be submitted electronically by October 13, 2013 11:59 PM PDT to:

Suresh Venkatasubramanian
School of Computing
University of Utah
[email protected]

Proposals should be submitted in PDF format (for other formats please contact the tutorial chair first). Proposals should include the following:

• Basic information: Title, brief description, name and contact information for each tutor, length of the proposed tutorial. If the intended tutorial is expected to take longer than 2 hours a rationale is expected. Also identify any other venues in which the tutorial has been or will be presented.
  
  
• Audience: Proposals must clearly identify the intended audience for the tutorial (e.g., novice, intermediate, expert).
• What background will be required of the audience?
• Why is this topic important/interesting to the SIAM data mining community?
• What is the benefit to participants?
• Provide some informal evidence that people would attend (e.g., related workshops).
  
  
• Coverage: Enough material should be included to provide a sense of both the scope of material to be covered and the depth to which it will be covered. The more details that can be provided, the better (up to and including links to the actual slides or viewgraphs). Note that the tutors should not focus mainly on their own research results. If, for certain parts of the tutorial, the material comes directly from the tutors' own research or product, please indicate this clearly in the proposal.
  
  
• Biographies: Provide brief biographical information on each tutor (including qualifications with respect to the tutorial's topic).  

Timeline

• Submission : October 13, 2013 11:59 PM PDT
• Decision Notification : October 27, 2013
• Complete Set of Tutorial Viewgraphs (Slides): February 13, 2014