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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.

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.

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.

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

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.

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. 

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.

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. 

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.

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.