Follow Gregory Piatetsky, No. 1 on LinkedIn Top Voices in Data Science & Analytics

KDnuggets Home » News » 2014 » Aug » News, Features » KDD-2014 Awards Winners ( 14:n23 )

KDD-2014 Awards Winners


KDD-2014, the leading and the largest conference in data mining, data science, and knowledge discovery, recognizes the key researchers and contributors through several awards - read about the winners.



By Gregory Piatetsky, @kdnuggets, Aug 22, 2014.

KDD-2014, Data Mining for Social Good KDD-2014, 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, to be held August 24-27, 2014, New York, NY, USA is the the leading research conference in Data Mining, Data Science, and Knowledge Discovery, and with over 2,000 people expected, it will be the largest conference in the field up to now!

KDD 2014 will bring together researchers and practitioners from all aspects of data science, data mining, knowledge discovery, large-scale data analytics, and big data.

KDD will also recognize the leaders in research and community with several significant awards.

Innovation Award

This winner in 2014 is Prof. Pedro Domingos from U. Washington. The SIGKDD Innovation Award recognizes outstanding technical contributions to the field of knowledge discovery in data and data mining that have had a lasting impact. This award recognizes Pedro Domingos’s work in data stream analysis, cost-sensitive classification, adversarial learning, and Markov logic networks, as well as applications in viral marketing and information integration.

Read the KDnuggets interview with Pedro Domingos.

Service Award

Ted Senator is the winner of the 2014 Service Award. The Service Award recognizes individuals for their outstanding service contributions to the field of knowledge discovery in the community. Senator earned this award for his work influencing the direction of major conferences, helping define the distinction between research and applications in knowledge discovery, and highlighting the challenges specific to the field for outside entities.

New in 2014 are the Test of Time Awards.

The three Test of Time Awards are granted to papers from past KDD conferences beyond the last decade that have had a profound influence on the data mining research community. The winning papers are as follows:

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, by Martin Ester, Hans-Peter Kriegel, Jiirg Sander, and Xiaowei Xu [KDD 1996]. This paper introduced density-based clustering to the data mining community. Since its introduction, density-based clustering has become one of the prominent clustering paradigms. Since the publication of DBSCAN, density-based clustering has been extensively studied and has been successfully used in many applications.

Integrating Classification and Association Rule Mining, by Bing Liu, Wynne Hsu, and Yiming Ma [KDD 1998]. This paper pioneered the research of using association rules for classification by integrating classification and association rule mining. It also proposed an efficient algorithm and built the first system (called CBA) for the purpose. This work triggered a large number of follow-up works and applications.

Maximizing the Spread of Influence through a Social Network, by David Kempe, Jon Kleinberg, and Eva Tardos [KDD 2003]. This paper considers questions involving the spread of information, innovations, and behaviors in social networks. The paper identifies a technically rich structure inherent in the problem, and establishes a framework that has subsequently been used in areas ranging from social media and marketing to the diffusion of innovations and the study of inequality.

Best Paper Awards

The overall best research track paper was Reducing the Sampling Complexity of Topic Models, by Aaron Q Li, Amr Ahmed, Sujith Ravi, and Alexander J Smola, which presents an approximate sampler for topic models that theoretically and experimentally outperforms existing samplers thereby allowing topic models to scale to industry-scale datasets.

The best student paper was An Efficient Algorithm For Weak Hierarchical Lasso, by Yashu Liu, Jie Wang, and Jieping Ye, which presents algorithms for tackling the non-convexity that arises in using the hierarchical lasso when regularizing parameters of models that attempt to capture non-linear feature interaction.

Best Social Good Paper
Targeting Direct Cash Transfers to the Extremely Poor,
by Brian Abelson, Enigma; Kush R Varshney, IBM TJ Watson; Joy Sun, GiveDirectly;

Best Best Industry and Government Track Paper
Style in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-commerce
by Diane J. Hu, Rob Hall, and Josh Attenberg (Etsy)

SIGKDD Dissertation Awards
Winner: Reconstruction and Applications of Collective Storylines from Web Photo Collections.
Gunhee Kim (student) and Eric Xing (advisor)

Runner-up: Human-Powered Data Management.
Aditya Parameswaran (student) and Hector Garcia-Molina (advisor)

Related:

Sign Up