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ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. 

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.

Jian Pei is the winner of its 2015 Service Award for his significant technical contributions to the principles, practice and application of data mining and for his outstanding services to society and the data mining community. Pei has a long history of contributing to the frontier of data mining research and serving the data mining community. As one of the most cited authors in data mining, Pei is one of the key organizers of many KDD and data mining conferences and events, such as ACM KDD, IEEE ICDM, SIAM Data Mining, and ACM CIKM, in various roles, such as general co-chair, program committee co-chair, tutorial co-chair, workshop co-chair, and senior program committee member.

ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. 

As we approach KDD-2015, the largest and highest quality conference on Data Mining, Data Science, and Knowledge Discovery, we want to introduce you to the amazing invited speakers we have lined up in the Industry and Government invited talks program that focuses on applications: deployed, real-world applications in industry and government, with quantifiable value delivered. These talks presents a rare opportunity  ...

2014 SIGKDD Test of Time Award:

The SIGKDD Test of Time award recognizes outstanding papers from past KDD Conferences beyond the last decade that have had an important impact on the data mining research community.

The 2014 Test of Time award recognizes the following influential contributions to

SIGKDD Dissertation Awards (1 winner, 1 runner-up and 3 honorable mentions)

ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. 

Selection Procedure: We received 19 nominations this year, a new record

ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining.

ACM SIGKDD is pleased to announce that Ted Senator is the winner of its 2014 Service Award for his contributions to society and the data mining community.  

Senator has a long history of serving the data mining community. His service has impacted the direction of major conferences that helped

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.