NEW YORK, NY- (July 28, 2016) - 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 the results of their prestigious awards granted in advance of the 22nd annual KDD-2016 conference in San Francisco, CA, August 13-17, 2016.
ACM SIGKDD has named Philip S. Yu the winner of its 2016 Innovation Award. Yu has been recognized for his influential research and scientific contributions on mining, fusion and anonymization of big data. This award is the highest honor for technical excellence in the field of Knowledge Discovery and Data Mining (KDD). It is bestowed on one individual-- or one group of collaborators-- whose outstanding technical innovations in the KDD field have had a lasting impact on 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 tremendous and innovative ways and/or enabled the development of commercial systems.
The ACM SIGKDD Service Award is the highest service award in the field of knowledge discovery and data mining. 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. This year, Wei Wang has received the award for her exceptional technical contributions to the foundation and practice of data mining, and for her impactful services to the data mining community. Wang has a long history of serving and promoting the data mining field. As a leading researcher in data mining, she has served as a key organizer in major data mining conferences, including ACM KDD, ICDM and SIAM Data Mining, and she has also served in more than 100 program committees.
The ACM SIGKDD Test of Time award recognizes outstanding papers from past KDD Conferences beyond the last decade that have had a strong impact on the data mining research community. The 2016 Test of Time winners include: Jure Leskovec - Stanford University, Jon Kleinberg - Cornell University and Christos Faloutsos - Carnegie Mellon University. Their 2005 paper-- Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations-- made new discoveries about how real-world graphs and networks grow and evolve over time. These discoveries fundamentally shaped the understanding of the evolution and growth of real-world networks, and the paper spurred a rich line of research on measuring and modeling the structure and evolution of networks across many domains.
The ACM SIGKDD dissertation award recognizes outstanding work done by graduate students in the areas of data science, machine learning and data mining. This year, Carnegie Mellon University student Danai Koutra and advisor Christos Faloutsos earned the honor for their work, Exploring and Making Sense of Large Graphs. Runner ups were University of California, Santa Barbara student Huan Sun and advisor Xifeng Yan, for their research Mining Disparate Sources for Question Answering. University of Southern California student Mohammad Taha Bahadori and advisor Yan Liu were also announced as runner ups for their work, titled, Scalable Multivariate Time Series Analysis.
Additionally, the SIGKDD Best Paper award winners were announced, which recognizes papers presented at the annual SIGKDD conference that advance the fundamental understanding of the field of knowledge discovery in data and data mining. The Research Track Best Paper award went to Carnegie Mellon University’s Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin and Christos Faloutsos for their paper, FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. Runner ups, for their paper, Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations, included NEC Laboratories’ Wei Cheng, Kai Zhang, Haifeng Chen, Guofei Jiang, Zhengzhang Chen and Wei Wang - UCLA.
Research Track Best Paper student award went to Lorenzo De Stefani - Brown University, Alessandro Epasto - Google, Matteo Riondato - Two Sigma Investments and Eli Upfal - Brown University, for their paper titled, TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size. Runner ups announced included Cornell University’s Shuo Chen and Thorsten Joachims, for their paper, Predicting Matchups and Preferences in Context.
Also announced, for best papers in the Applied Data Science track, were Yahoo Inc.’s Dawei Yin, Yuening Hu, Yi Chang, Tim Daly, Mianwei Zhou, Jianhui Chen, Changsung Kang and Jean-Marc Langlois; Apple’s Hua Ouyang, Chikashi Nobata, Hongbo Deng - Google and Jiliang Tang - Michigan State University for their paper titled, Ranking Relevance in Yahoo Search. Runner ups for the award were Reza Zadeh - Stanford University, Xiangrui Meng - Databricks, Alexander Ulanov - HP Labs, Burak Yavuz - Databricks, Li Pu - Twitter, Shivaram Venkataraman - UC Berkeley, Evan Sparks, Aaron Staple and Matei Zaharia - MIT and Databricks, for their paper, Matrix Computations and Optimization in Apache Spark.
Best student paper award in the applied data science track went to Yu Sun - University of Melbourne, Nicholas Jing Yuan - Microsoft Corporation, Yingzi Wang - University of Science and Technology of China & Microsoft Research, Xing Xie - Microsoft Research, Kieran McDonald - Microsoft Corporation and Rui Zhang - University of Melbourne, for their paper titled, Contextual Intent Tracking for Personal Assistants. Runner ups for the student paper were Michael Madaio - Carnegie Mellon University, Shang-Tse Chen - Georgia Institute of Technology, Oliver L. Haimson - University of California, Irvine, Wenwen Zhang - Georgia Institute of Technology, Xiang Cheng - Emory University, Matthew Hinds-Aldrich - Atlanta Fire Rescue Department, Duen Horng Chau - Georgia Institute of Technology and Bistra Dilkina - Georgia Institute of Technology, for their paper, Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta.
Registration is still open for the conference, taking place August 13-17, 2016 at the Hilton SF, Union Square: https://www.regonline.com/Register/Checkin.aspx?EventID=1832521
ACM SIGKDD, which stands for Special Interest Group for Knowledge Discovery from Data, is a professional society comprising of world-renowned data scientists from industry and academia. KDD is the annually held, premier international conference that brings together researchers and practitioners from both academia and industry to deep-dive into novel ideas, latest research results and share in-the-trenches experiences and innovations. More details can be found at kdd.org.