SIGKDD Innovation Award

The award recognizes individuals for their outstanding technical contributions to the field of knowledge discovery in data and data mining that have had lasting impact in furthering the theory and/or development of commercial systems.

     Frequency of the Awards

Once a year.
     Administration of the Awards Program
The SIGKDD Awards Committee, consisting of 3-5 prominent senior scientists in the field, will solicit nominations for recipient candidates, evaluate the nominations, and select winners.
The SIGKDD Chair will form the Awards Committee by inviting candidates. The SIGKDD Chair will appoint the Chair of the Awards Committee.
The terms of the Awards Committee will be the same as the term of the SIGKDD Chair.
Once formed, the Awards Committee will select winners of the Awards completely independently of SIGKDD Chair or SIGKDD Executive Committee.
The Award winner will be decided by a two-thirds majority vote of the Awards Committee.
There will be at most one individual or one group to receive either Award in any given year. (It is possible that in a given year, there may be no winner of either Award.)
The Awards Committee will solicit nominations for Award recipients 5 months before the SIGKDD Annual International Conference via the SIGKDD website, SIGKDD Annual Conference website, and the KDNuggets electronic newsletter.
Nominations, once made, may be re-considered for the subsequent two years; if the nominee does not win after the first three years, the nomination is discarded.
The deadline for the nominations will be 3 months before the SIGKDD Annual International Conference. (The Awards Committee will take 6 weeks to make its decisions.)
The winners will receive the Awards at the SIGKDD Annual International Conference. The winners will be announced in the SIGKDD Conference website and the SIGKDD website.
     The Awards
Each Award carries a $2,500 monetary award and a plaque.
If the winner is a group of individuals, the group will receive $2,500 (not each individual). However, each individual will receive a plaque.
SIGKDD Chair and members of the SIGKDD Awards Committee are not eligible to be nominated for either Award.
2014 ACM SIGKDD Awards Committee
  • Gabor Melli (VigLink) – Chair of Awards Committee
  • Rakesh Agrawal (Microsoft)
  • Jon Kleinberg (Cornell University)
  • Sunita Sarawagi (Indian Institute of Technology)
  • Bharat Rao (Deloitte)
  • Christos Faloutsos (Carnegie Mellon University)
  • Vipin Kumar (University of Minnesota)
  • Ying Li (EV Analysis Corporation)
  • Osmar Zaiane (University of Alberta)
  • Usama Fayyad (Barclays)
2013 ACM SIGKDD Awards Committee
  • Bharat Rao (Deloitte)
  • Christos Faloutsos (Carnegie Mellon University)
  • Osmar Zaiane (University of Alberta)
  • Padhraic Smyth (University of California, Irvine)
  • Ross Quinlan (Rulequest Research)
  • Ramasamy Uthurusamy (General Motors Corporation)
  • Sunita Sarawagi (Indian institute of technology)
  • Usama Fayyad (ChoozOn Corporation)
  • Vipin Kumar (University of Minnesota)
  • Ying Li (Concurix Corporation) – Chair of Awards Committee
2012 ACM SIGKDD Awards Committee
  • Ramasamy Uthurusamy, Chair Chid Apte, IBM Research
  • Christos Faloutsos, Carnegie Mellon University
  • Bing Liu, University of Illinois at Chicago
  • Gregory Piatetsky-Shapiro, KD Nuggets
  • Daryl Pregibon, Google
  • J. Ross Quinlan, Rulequest
  • Ted Senator, SAIC
  • Padhraic Smyth, University of California at Irvine
  • Qiang Yang, Hong Kong University of Science and Technology
  • Osmar R. Zaiane, University of Alberta C Past Chair
2011 ACM SIGKDD Awards Committee
  • Osmar R. Zaïane, Chair
  • Ramasamy Uthurusamy, Past Chair
  • Christos Faloutsos (Carnegie Mellon University)
  • Peter Flach (University of Bristol)
  • Robert Grossman (University of Illinois at Chicago)
  • Ying Li (Microsoft)
  • Bing Liu (University of Illinois at Chicago)
  • Sunita Sarawagi (Indian Institute of Technology, Bombay)
  • Padhraic Smyth (University of California at Irvine)
  • Qiang Yang (Hong Kong UST) 



Pedro Domingos

ACM SIGKDD is pleased to announce that Pedro Domingos is the winner of its 2014 Innovation Award. He is recognized for his foundational research in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks, as well as applications in viral marketing and information integration.
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...

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Prof. Jon Kleinberg

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 Innovation Award is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is conferred...

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Prof. Vipin Kumar

ACM SIGKDD is pleased to announce that Prof. Vipin Kumar is the winner of its 2012 Innovation Award.

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.

Prof. Vipin Kumar is recognized for his technical...

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Dr. J. Ross Quinlan

ACM SIGKDD is pleased to announce that Dr. Ross Quinlan is the winner of the 2011 Innovation Award. He 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. The first of these, a...

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Dr. Christos Faloutsos

Professor in Electrical and Computer Engineering
Carnegie Mellon University
ACM SIGKDD is pleased to announce that Prof. Christos Faloutsos is the winner of its 2010 Innovation Award. He 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.
Professor Faloutsos seminal cross-disciplinary works...

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Dr. Padhraic Smyth

Professor in the Department of Computer Science; and Director. Center for Machine Learning and Intelligent Systems, University of California, Irvine
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 (...

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Dr. Raghu Ramakrishnan

Chief Scientist of Audience, and Research Fellow at Yahoo! Research
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...

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Dr. Usama M. Fayyad

Chief Data Officer and Executive Vice President for Research & Strategic Data Solutions at Yahoo!
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...

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Dr. Ramakrishnan Srikant

Research Scientist at Google
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...

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Dr. Leo Breiman

Professor Emeritus at the University of California, Berkeley
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 (Classification and Regression Trees, 1984, known as CART(R)), written with Jerome Friedman, Richard Olshen, and Charles Stone....

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Dr. Jiawei Han

Professor in the Department of Computer Science
University of Illinois at Urbana-Champaign
The 2004 SIGKDD Innovation Award went to Jiawei Han, Professor, Computer Science, at Univ. of Illinois at Urbana-Champaign.

Dr. Han is widely and well regarded as a pioneer researcher in data mining and knowledge discovery, who has made many fundamental research contributions, including

  • Novel and efficient algorithms for frequent pattern mining, e.g.,...
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Dr. Heikki Manilla

Professor at Helsinki University of Technology
The winner of the SIGKDD 2003 Innovation Award is Professor Heikki Mannila, Professor (Helsinki University of Technology) and Research Director, HIIT Basic Research Unit, University of Helsinki & Helsinki University of Technology, Finland. The award carries with it a memorial plaque and a check for $2,500.
Professor Mannila has the rare virtue of being able to identify new problems, viewpoints, and concepts, and...

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Dr. Jerome H. Friedman

Professor in the Department of Statistics, Stanford University;
Leader, Computation Research Group at Stanford Linear Accelerator Center
Jerry Friedman has contributed a remarkable array of topics and methodologies to data mining and machine learning during the last 25 years.

In 1977, as leader of the numerical methods group at the Stanford Linear Accelerator Center (SLAC), he coauthored several algorithms for speeding up nearest-neighbor classifiers.

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Dr. Rakesh Agrawal

IBM Fellow
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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