2013 Doctoral Dissertation Award
This annual award introduced in 2008 recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery.
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2013 SIGKDD Doctoral Dissertation Award
CALL FOR NOMINATIONS
Nomination Deadline: May 17, 2013
http://kdd.org/awards_dissertation.php
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This annual award by ACM SIGKDD will recognize excellent research
by doctoral candidates in the field of data mining and knowledge
discovery. The KDD Doctoral Dissertation Award winner and up to two
runners-up will be recognized at the KDD conference, and their
dissertations will have the opportunity to be published on the KDD
Web site (http://www.kdd.org). The award winner will receive a plaque,
a check for $2,500, and will be invited to present his or her work at the
KDD conference. The award winner will also receive a free registration
to attend the KDD conference. The runners-up will receive a plaque at
the conference.
Eligibility
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The final dissertation defense should take place at the nominee's host
institution before the submission deadline. Furthermore, the final
dissertation defense must not have taken place prior to January 1st, 2012.
Nominations are limited to one doctoral dissertation per department or
academic unit. Submissions must be received by the submission deadline.
Each nominated dissertation must also have been successfully defended
by the candidate, and the final version of each nominated dissertation
must have been accepted by the candidate's academic unit. An English
version of the dissertation must be submitted with the nomination. A
dissertation can be nominated for both the SIGKDD Doctoral
Dissertation Award and the ACM Doctoral Dissertation Award.
Important Dates
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Submission Deadline: May 17, 2013.
Notification of Awards: July 12, 2013.
Award Presentation at KDD 2013: August 11-14, 2013, Chicago, USA.
Submission Procedure
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All nomination materials must be submitted electronically to:
mobasher [at] cs.depaul.edu
Please use "SIGKDD Dissertation Award Nominations" in you subject
line.
All nomination materials must be in English. PDF format is preferred
for all materials. Late submissions will not be accepted. A nomination
must include:
1. A nomination letter, written by the dissertation advisor of the
candidate. This letter must include full contact information for both
the advisor and the nominee as well as a one- or two-page summary of
the significance of the dissertation.
2. An endorsement letter signed by the department head.
3. One PDF copy of the doctoral dissertation.
4. A copyright transfer form signed by the candidate is required
giving permission for the dissertation to appear on KDD.org Web site
if the dissertation is selected as an award recipient (but if the
nomination is also being submitted for the ACM Doctoral Dissertation
Award, only one form needs to be signed). See:
http://www.acm.org/pubs/copyright_form.html
5. Optionally, the nomination may include up to two supporting letters
from other individuals, discussing the significance of the
dissertation.
Additional information is available at:
http://kdd.org/awards_dissertation.php
Please direct questions to the Award Committee Chair: Bamshad Mobasher,
DePaul University, mobasher [at] cs.depaul.edu.
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Recipients
2012
Modeling Content and Users: Structured Probabilistic Representation and Scalable Inference Algorithms
Efficient Algorithms for Detecting Genetic Interactions in Genome-Wide Association Study
2011
Dr. Hay's dissertation presents several discoveries and innovations in the area of privacy-preserving analysis of relational data. These findings are likely to have important implications for preserving personal privacy while analyzing data in social networks.
Data Mining Techniques for Enhancing Protein Function Prediction
Correlation Analysis: From Computation Hardness to Practical Success
Constraint-Based Mining of Closed Patterns in Noisy N-Ary Relations
2010
Dr. Mohammad Al Hasan's dissertation presents an innovative and general approach to frequent pattern mining with a potential to dramatically improve the performance of currently available tools.
Contextual Text Mining
Matrix Decomposition Methods for Data Mining: Computational Complexity and Algorithms
2009
From Itemsets Through Trajectories to Location Based Services: A Knowledge Hiding Privacy Approach
Towards Accurate and Efficient Classification: A Discriminative and Frequent Pattern-Based Approach
2008
Building Acceptable Classification Models for Financial Engineering Applications
Approximate inference, structure learning and feature estimation in Markov Random Fields













