Features

May 21, 2009. KDD-2009, the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, to be held in Paris, France, June 28-July 1, 2009, will highlight the second annual ACM SIGKDD Doctoral Dissertation Award presentations. The award seeks to recognize outstanding research by doctoral candidates in the field of data mining and knowledge discovery. Since its inception in 2008, the interest in the Award program has increased dramatically resulting in a number of very high quality nominations from prominent universities and institutions across the world.

The SIGKDD Dissertation Award Committee is pleased to announce that the winner is

Dr. Jure Leskovec, for his dissertation "Dynamics of Large Networks" (advisor: Christos Faloutsos, Carnegie Mellon University).

Excerpt: we found very interesting and counterintuitive patterns for time evolving networks, which change some of the basic assumptions that were made in the past. He also developed models that explain processes which govern the network evolution, fit such models to real networks, and use them to generate realistic graphs or give formal explanations about their properties. His work has a wide range of applications, including spotting anomalous graphs and outliers, forecasting future graph structure and simulations of network evolution.

Dr. Leskovec's dissertation was selected from among a number of very strong candidates and receiving this award serves as a clear recognition of its contributions to the KDD community.

SIGKDD will also recognize the runner up dissertation by Dr. Arthur Zimek, titled "Correlation Clustering" (advisor: Hans-Peter Kriegel, Ludwig Maximilians University, Germany).

Excerpt: His thesis offers a systematic classification of the diverse approaches to clustering according to their task definition, their basic strategy, and their algorithmic approach. The main categories are the search for clusters (i) w.r.t. closeness of objects in axis-parallel subspaces, (ii) w.r.t. common behavior (patterns) of objects in axis-parallel subspaces, and (iii) w.r.t. closeness of objects in arbitrarily oriented subspaces (so called correlation cluster). For the third category, the remaining parts of the thesis describe novel approaches. A first approach is the adaptation of density-based clustering to the problem of correlation clustering.

Both the winner and the runner up will be recognized at the KDD conference opening ceremonies with a plaque. The winner will receive $2,500 honorarium and will have the opportunity to present a summary of the dissertation during the opening ceremonies.

This year, the Dissertation Award Committee has also chosen to recognize two additional finalists from among the candidates because of the excellent contributions made by their dissertations. These finalists are

• Dr. Aris Gkoulalas-Divanis (Dissertation: "From Itemsets Through Trajectories to Location Based Services: A Knowledge Hiding Privacy Approach"; Advisors: Elias Houstis and Vassilios Verykios, University of Thessaly, Greece) and
• Dr. Hong Cheng (Dissertation: "Towards Accurate and Efficient Classification: A Discriminative and Frequent Pattern-Based Approach"; Advisor: Jiawei Han, University of Illinois at Urbana-Champaign). Both finalists will also be recognized at the opening ceremonies and will receive a certificate of recognition.

http://www.sigkdd.org/awards_dissertation.php

Dissertation Awards Committee

• Bamshad Mobasher, Chair, DePaul University, USA
• Tanya Berger-Wolf, University of Illinois, Chicago
• Christos Faloutsos, Carnegie Mellon University, USA
• C. Lee Giles, Pennsylvania State University, USA
• Dimitrios Gunopulos, UC Riverside, USA
• Ee-Peng Lim, Nanyang Technological University, Singapore
• Jennifer Neville, Purdue University
• Myra Spiliopoulou, Otto von Guericke University Magdeburg, Germany
• Ming-Syan Chen, National Taiwan University
• Hannu Toivonen, University of Helsinki, Finland
• Shusaku Tsumoto, Shimane University, Japan
• Jaideep Vaidya, Rutgers University
• Xiaoxin Yin, Microsoft