SIGKDD Awards

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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Prof. Vipin Kumar is recognized for his technical contributions to foundational research in data mining as well as its applications to mining scientific data. Prof. Kumar has made numerous significant and impactful contributions to a wide range of core data mining areas including graph partitioning, clustering, association analysis, high performance and parallel data mining, anomaly/change detection and data driven discovery methods for analyzing global climate and ecosystem data. Many of his papers on these topics are amongst the most highly cited papers in data mining.

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Dr. Ross Quinlan is recognized for his seminal contributions to rule induction and decision tree algorithms and for participating in laying the foundation of data mining, particularly with the invention of ID3 and C4.5, algorithms pivotal in myriad applications. Dr. Ross Quinlan is best known for the development of programs for machine learning and data mining. 

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Prof. Christos Faloutsos is recognized for his fundamental contributions to graph and multimedia mining, fractals, self-similarity and power laws; indexing for multimedia and bioinformatics data, and data base performance evaluation. His seminal cross-disciplinary works on power-law graphs, fractal-based analysis, time series, multimedia and spatial indexing are a rare combination of both impressive breadth as well as fundamental depth that set new research directions and inspired subsequent research impacting the KDD field.

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Padhraic Smyth is recognized for his contributions to both the theory and application of probabilistic and statistical approaches to data mining. Smyth’s research is in the area of statistical data mining and machine learning. His research focuses on both the basic principles of inference from data (theory and algorithms), combined with applications to a variety of data-driven problems in the sciences, medicine, and engineering. 

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Ramakrishnan's contributions span foundational technical innovation on algorithmic and systems aspects of data mining. His work on scalable data mining algorithms started with BIRCH, the first truly scalable clustering algorithm. BIRCH introduced the groundbreaking idea of a cluster feature, a concise summary of a cluster, which was then used in many subsequent clustering algorithms as an integral component.

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Fayyad is recognized for his seminal work on the development data mining, machine learning algorithms and their scalability to massive database systems, and fundamental applications of data mining in scientific discovery and commercial database systems. His contributions span fundamental technical innovation and significant large-scale applications of the technology in science data analysis, commercial practice, and commercial database systems.

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Srikant identified novel pruning techniques and data structures that made the discovery of association rules feasible. He also generalized association rules along three orthogonal dimensions: discovering associations across different levels of a hierarchy over the items; discovering temporal associations ("sequential patterns"); and discovering associations over quantitative attributes. In each case, Srikant invented pruning techniques and data structures that kept the execution times practical.

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Dr. Leo Breiman is widely considered one of the founding fathers of modern machine learning and data mining. He has been actively contributing to these fields, as well as to statistics, for more than 30 years. His best known contribution is his landmark work on decision trees.

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Dr. Han is widely and well regarded as a pioneer researcher in data mining and knowledge discovery, who has made many fundamental research contributions. He has published more than 100 research papers on data mining in leading database and data mining conferences and journals. His contribution can be seen in almost every area of the field. Dr. Han is a very highly cited author, with over 3,000 citations, indicating the quality of his work, his influence in the field, and his contributions to many topics of data mining.

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Professor Mannila has the rare virtue of being able to identify new problems, viewpoints, and concepts, and thereby taking the field forward. He introduced the concept of "inductive databases" that integrate data mining and databases. Equally impressive are Professor Mannila's contributions in providing a substantial and much needed theoretical foundation in a very young field. He has given strong theoretical results for many data mining problems, including association rules and frequent time sequences. The breadth of Prof Mannila's work is quite spectacular. He has over 130 articles in journals and refereed conferences covering such diverse topics as association rules, probabilistic modeling, inductive databases, similar time series, and bio-informatics.

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Jerry Friedman has contributed a remarkable array of topics and methodologies to data mining and machine learning during the last 25 years. Taken together, Dr. Friedman's list of contributions to new methodology, including CART, MARS, PRIM, PPR, and Gradient Boosting, constitutes one of the broadest ranges of any one person in the field.

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Rakesh Agrawal from IBM has received the first ACM SIGKDD Award for Innovation for his many research contributions, including his pioneering work on association rules, mining sequences and much more.

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