SIGKDD Awards

ACM SIGKDD is pleased to announce that George Karypis is the winner of its 2022 Innovation Award. George Karypis' research interests are concentrated in the areas of data mining, recommender systems, learning analytics, high-performance computing, and chemical informatics and from time-to-time, he looks at various problems in the areas of health informatics, information retrieval, bioinformatics, and scientific computing. Within these areas, his research focuses in developing novel algorithms for solving important existing and/or emerging problems, and on developing practical software tools implementing some of these algorithms. The results from this research have been presented in various conferences and published in leading peer reviewed journals and highly selective conference proceedings. 

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ACM SIGKDD is pleased to announce that Lilsa Getoor is the winner of its 2024 Innovation Award. Lise Getoor is Distinguished Professor of Computer Science and Jack Baskin Chair of Computer Engineering at University of California, Santa Cruz. Her research is in the areas of machine learning, artificial intelligence, and probabilistic reasoning – making decisions in situations of uncertainty. She works with graph-structured, semi-unstructured and unstructured data sets to create and employ statistical models. Her research mixes tools from different areas of computer science to deal with the challenges of incompleteness, uncertainty, and bias in data.

Getoor is also an advocate for ethics and responsible data science, addressing both the technical and societal implications of emerging data science technology. This work tackles issues of privacy and bias in new and powerful algorithmic tools. She is considered a leader in this field and has convened groups and given multiple keynote talks at various conferences on the subject.

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ACM SIGKDD is pleased to announce that Jure Leskovec is the winner of its 2023 Innovation Award. Jure Leskovec is Professor of Computer Science at Stanford University. He is affiliated with the Stanford AI Lab, Machine Learning Group and the Center for Research on Foundation Models. In the past, he served as a Chief Scientist at Pinterest and was an investigator at Chan Zuckerberg Biohub. Most recently, he co-founded machine learning startup Kumo.AI. Leskovec pioneered the field of Graph Neural Networks and co-authored PyG, the most widely-used graph neural network library. Research from his group has been used by many countries to fight COVID-19 pandemic, and has been incorporated into products at Facebook, Pinterest, Uber, YouTube, Amazon, and more.

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ACM SIGKDD is pleased to announce that Dr. Huan Liu is the winner of its 2022 Innovation Award.  He is a professor of computer science and engineering at Arizona State University, Tempe and is recognized for his outstanding and influential contributions to the foundation, principles and applications of social media mining and feature selection for data mining.

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Johannes Gehrke is a Technical Fellow and the Managing Director of Research at Redmond and the CTO and head of machine learning for the Intelligent Communications and Conversations Cloud (IC3), which powers Microsoft Teams. Until June 2020, he was leading architecture and machine learning for IC3. Johannes’ research interests are in the areas of database systems, distributed systems, and machine learning. He has received a National Science Foundation Career Award, an Arthur P. Sloan Fellowship, a Humboldt Research Award, the 2011 IEEE Computer Society Technical Achievement Award, and he is an ACM Fellow and an IEEE Fellow. Johannes co-authored the undergraduate textbook Database Management Systems (McGrawHill (2002), currently in its third edition), used at universities all over the world. He is a member of the ACM SIGKDD Executive Committee. From 1999 to 2015, Johannes was on the faculty in the Department of Computer Science at Cornell University where he graduated 25 PhD students, and from 2005 to 2008, he was Chief Scientist at FAST Search and Transfer.

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Thorsten Joachims, professor of Computer Science and Information Science at Cornell University, is recognized for his research contributions in machine learning, including influential work studying human biases in information retrieval, support vector machines (SVM) and structured output prediction. Notably, Joachims pioneered methods for eliciting reliable preferences from implicit feedback, methods for unbiased learning-to-rank and ranking methods that provide fairness guarantees. The ACM SIGKDD Innovation Award is the highest honor for technical excellence in the field of knowledge discovery and data mining. It is conferred on an individual or group of collaborators whose outstanding technical innovations have greatly influenced the direction of research and development in the field.

“I am greatly honored by this recognition from the KDD community,” said Joachims. “KDD is known for innovation — not only as an academic endeavor but also with an eye towards real-world impact and social good.”

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ACM SIGKDD is pleased to announce that Dr. Charu Aggarwal is the winner of its 2019 Innovation Award. He is a distinguished research staff member at IBM T.J. Watson Research Center and is recognized for his research contributions in high-dimensional data, privacy, data streams, uncertain data, graphs, text mining and social networks.

The ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field. The contributions must have significantly influenced the direction of research and development of the field or transferred to practice in significant and innovative ways and/or enabled the development of commercial systems.

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ACM SIGKDD is pleased to announce that Dr. Bing Liu is the winner of its 2018 Innovation Award. He is a Distinguished Professor of Computer Science at the University of Illinois at Chicago and is honoured this year for his seminal contributions to the foundation of data mining and applications, particularly in opinion mining. 

The ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field. The contributions must have significantly influenced the direction of research and development of the field or transferred to practice in significant and innovative ways and/or enabled the development of commercial systems.

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ACM SIGKDD is pleased to announce that Dr. Jian Pei is the winner of its 2017 Innovation Award.  He is recognized for his seminal contributions to the foundation of data mining and applications, particularly in pattern mining and spatial data mining.  He is a major inventor of several pattern-growth methods, including FP-growth and PrefixSpan, which have been extensively used by industry and adopted by data mining textbooks and open source software toolkits. As one of the most cited authors in data mining, his prolific publications have been cited tens of thousands of times.

The ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field. The contributions must have significantly influenced the direction of research and development of the field or transferred to practice in significant and innovative ways and/or enabled the development of commercial systems.

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ACM SIGKDD is pleased to announce that Philip S. Yu is the winner of its 2016 Innovation Award.  He is recognized for his influential research and scientific contributions on mining, fusion and anonymization of big data.

The ACM SIGKDD Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred on one individual or one group of collaborators whose outstanding technical innovations in the KDD field have had a lasting impact in advancing the theory and practice of the field. The contributions must have significantly influenced the direction of research and development of the field or transferred to practice in significant and innovative ways and/or enabled the development of commercial systems.

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Hans-Peter Kriegel is the winner of its 2015 Innovation Award.  He is recognized for his influential research and scientific contributions to data mining in clustering, outlier detection and high-dimensional data analysis, including density-based approaches. He has been a Professor of Informatics at Ludwig-Maximilians-Universitaet Muenchen, Germany since 1991. He has published over a wide range of data mining topics including clustering, outlier detection and high-dimensional data analysis. In 2009 the Association for Computing Machinery (ACM) elected Professor Kriegel an ACM Fellow for his contributions to knowledge discovery and data mining, similarity search, spatial data management, and access-methods for high-dimensional data.

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Prof. Domingos carried out some of the earliest research on mining data streams. His VFDT algorithm was the first to be capable of learning decision trees from streams while guaranteeing that the result is very close to that of batch learning, and remains the fastest decision tree learner available. He went on to generalize the ideas in VFDT to clustering, the EM algorithm, Bayesian network structure learning, and other problems. The resulting VFML toolkit is one of the best open-source resources for stream mining.

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ACM SIGKDD is pleased to announce that Prof. Jon Kleinberg is the winner of the 2013 Innovation Award. He is recognized for his seminal contributions to the analysis of social and information networks, mining the web graph, study of cascading behaviors in networks, and the development of algorithmic models of human behavior.

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