ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining.
The 2016 Test of Time award recognizes the following influential contributions to SIGKDD that have withstood the test of time.
ACM SIGKDD is pleased to announce that Wei Wang is the winner of its 2016 Service Award for her exceptional technical contributions to the foundation and practice of data mining and for her excellent services to the data mining community.
ACM SIGKDD Service Award is the highest service award in the field of knowledge discovery and data mining (KDD). It is conferred on one individual or one group for their outstanding professional services and contributions to the field of knowledge discovery and data mining
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.
By Evangelos Simoudis, Founder and Managing Director at Synapse Partners, on behalf of the KDD-2016 Conference
Big data’s day has come: it dominates headlines and business conversations alike. As industries coalesce around it, the conversations surrounding big data (benefits, issues, challenges)
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NEW YORK, NY— 5/23/2016- The Association of Computing Machinery’s Special Interest Group for Knowledge Discovery and Data Mining (ACM SIGKDD), the world’s oldest and largest
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NEW YORK, NY—(TBD) - The Association of Computing Machinery’s Special Interest Group for Knowledge Discovery and Data Mining (ACM SIGKDD), the world’s oldest and largest community for data mining, data science and analytics, today announced its 22nd annual conference will take place Aug. 13-17, 2016 at the Hilton Hotel in San Francisco, CA. The event’s main focus is to connect the world’s best data scientists with one another in order to discuss, address and advance the application of data science to benefit all aspects of society.
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Nominations due May 20, 2016 (Friday)
ACM SIGKDD invites your nominations for its 2016 Innovation and Service Awards.
ACM SIGKDD, ACM’s Special Interest Group on Knowledge Discovery and Data Mining (KDD), is the premier global professional organization for researchers and professionals
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The KDD Cup, the most prestigious competition in KDD, is now under way. This year's competition aims at addressing a long-standing puzzle in academic society: how to rank an academic institution? It challenges the KDD Community to develop models to rank academic institutions based on their potential paper acceptance in the upcoming top-tier conferences. In effect, given a research field, we are challenging the KDD Cup community to jointly develop data mining techniques to identify the best research institutions based on their publications and how they are cited in research articles. Join us.
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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.
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.
The 2015 Test of Time award recognizes the following influential contributions to SIGKDD that have withstood the test of time.
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 ...
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The 2014 Test of Time award recognizes the following influential contributions to SIGKDD that have withstood the test of time:
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
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The award recognizes papers presented at the annual SIGKDD conference that advance the fundamental understanding of the field of knowledge discovery in data and data mining.
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
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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.
