SIGKDD Awards

ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. The original call for nomination is available here.

Review Criteria:

• Relevance of the Dissertation to KDD
• Originality of the Main Ideas in the Dissertation
• Significance of Scientific Contributions
• Technical Depth and Soundness of Dissertation (including experimental methodologies, theoretical results, etc.)
• Overall Presentation and Readability of Dissertation (including organization, writing style and exposition, etc.)

Congratulations to all the outstanding students who were nominated and to the winners of this year.

Following are the granted awards, including one winner, one runner-up, and two honorable mentions.

WINNER

Rediet Abebe, incoming assistant professor of Computer Science at the University of California at Berkeley, earned this year’s ACM SIGKDD Dissertation Award for her Ph.D. thesis, “Designing Algorithms for Social Good.” Abebe is the first female computer scientist to be inducted into the Harvard Society of Fellows and co-founded Mechanism Design for Social Good (MDSG), a multi-institutional initiative to improve access to opportunity for historically underserved and disadvantaged communities. 

RUNNER UP

Jingbo Shang, assistant professor of Computer Science at University of California at San Diego, earned runner-up for his thesis, “Constructing and Mining Heterogeneous Information Networks From Massive Text.” The ACM SIGKDD Dissertation Award recognizes outstanding work done by graduate students in the areas of data science, machine learning and data mining.