SIGKDD Awards

WINNER

Enabling Accurate Analysis of Private Network Data

Michael Hay (student), David Jensen and Gerome Miklau (advisors) at UMass Amherst

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.

RUNNER-UP

A Framework for Promotion Analysis in Multi-Dimensional Space

Tianyi Wu (student) and Jiawei Han (advisor) at University of Illinois Urbana-Champaign

CERTIFICATE OF RECOGNITION

Data Mining Techniques for Enhancing Protein Function Prediction

Gaurav Pandey and Vipin Kumar (advisor) at the University of Minnesota

HONORABLE MENTIONS

Correlation Analysis: From Computation Hardness to Practical Success

Wenjun Zhou (student) and Hui Xiong (advisor) at Rutgers University

Constraint-Based Mining of Closed Patterns in Noisy N-Ary Relations

Loic Cerf (student) and Jean-Françs Boulicaut (advisor)