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

Dissertation title: Efficient and Effective Learning of Text Representations
Yu Meng, UIUC (USA)

Yu Meng received his Ph.D. from the Department of Computer Science at University of Illinois Urbana-Champaign in 2023, where he worked with Jiawei Han. Prior to joining UVA, he was also a visiting researcher at Princeton University, working with Danqi Chen. He was awarded a Google Ph.D. Fellowship during his Ph.D. His research interests include machine learning, natural language processing, and data mining.

RUNNER UP

Dissertation title: Make Knowledge Computable: Towards Differentiable Neural-Symbolic AI
Ziniu Hu, UCLA (USA)

Ziniu Hu finished Postdoc at Caltech CMS hosted by Prof. Yisong Yue, during which he was also a visiting researcher at Google DeepMind. He received CS PhD degree at UCLA, where he had the fortune to be advised by Prof. Yizhou Sun and Prof. Kai-Wei Chang. He received his CS bachelor degree at Peking University, advised by Prof. Xuanzhe Liu. His research is generously supported by Amazon PhD Fellowship and Baidu Scholarship.

RUNNER UP

Dissertation title: Artificial Intelligence for Data-centric Surveillance and Forecasting of Epidemics
Alexander Rodriguez, Georgia Tech (USA)

Alexander’s work advances AI methods for modeling complex spatiotemporal dynamics to support trustworthy data-driven decision-making. His research addresses problems at the intersection of machine learning, time series analysis, scientific modeling (AI for science), uncertainty quantification, and multi-agent systems, with primary applications in health sciences and engineering. He graduated from Georgia Institute of Technology, advised by Prof. Aditya Prakash.