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: Computational Methods for Human Networks and High-Stakes Decisions
Serina Chang, Stanford University (USA)

Serina Chang is an Assistant Professor at UC Berkeley, jointly appointed in EECS and Computational Precision Health and part of the Berkeley AI Research (BAIR) Lab. Her research falls at the intersection of AI and human behavior, including modeling human behaviors with AI, improving and evaluating human-AI interaction, and developing AI tools for societal decision-making, with a focus on public health. Her work is recognized by the KDD Best Paper Award, Google Research Scholar Award, NSF Graduate Research Fellowship, Meta PhD Fellowship, EECS Rising Stars, and Rising Stars in Data Science, and has been featured by over 650 news outlets, including The New York Times and The Washington Post. Previously, she completed her PhD in CS at Stanford University, advised by Jure Leskovec and Johan Ugander, and she was a postdoc at Microsoft Research in the Computational Social Science group.

RUNNER UP

Dissertation title: Structure-Enhanced Text Mining For Science
Yu Zhang, UIUC (USA)

Yu Zhang is an Assistant Professor at the Department of Computer Science & Engineering at Texas A&M University, where he leads the SKY Lab. Prior to TAMU, Yu received his Ph.D. and M.Sc. degrees in Computer Science from the University of Illinois at Urbana-Champaign, advised by Prof. Jiawei Han. During his graduate study, he visited the University of Washington, working with Prof. Sheng Wang; he interned at Microsoft Research Redmond three times, working with Dr. Iris Shen, Dr. Hao Cheng, Dr. Xiaodong Liu, and Dr. Yuxiao Dong. Prior to UIUC, Yu received his B.Sc. degree in Computer Science from Peking University, advised by Prof. Yan Zhang.

HONORABLE MENTION

Dissertation title: Data Quality-Aware Graph Machine Learning​
Yu Wang, Vanderbilt University (USA)

Yu Wang is an assistant professor in the computer science department of the School of Computer and Data Science at the University of Oregon. Yu received his PhD degree from Vanderbilt University under the supervision of Prof. Tyler Derr.

HONORABLE MENTION

Dissertation title: Trustworthy Transfer Learning​
Jun Wu, UIUC (USA)

Jun Wu is an Assistant Professor in the Department of Computer Science and Engineering at the Michigan State University (MSU). Before joining MSU, he received his Ph.D. degree in Computer Science at the University of Illinois Urbana-Champaign. He has a broad research interest in trustworthy machine learning, transfer learning/domain adaptation, and graph learning, with applications in agriculture, bioinformatics, e-commerce, and legal analytics. His work has been recognized by the 2025 AAAI New Faculty Highlights.