Data Mining/Data Science “Nobel Prize”: 2016 SIGKDD Innovation Award to Philip S. Yu
Dr. Philip S. Yu wins ACM KDD Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data.
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.
Philip S. Yu has made many tremendously influential, critical and well-recognized contributions to the principles of KDD and data mining over the years. He has been working on big data related issues long before the term “big data” has come into vogue in recent years. He has published more than 900 papers receiving more than 73,000 citations and contributing to the various aspects of knowledge discovery, including frequent pattern mining, clustering, classification, outlier detection, recommendation, feature selection, similarity search, spam detection, and data anonymization. His works mainly focus on mining non-conventional types of data, including data streams, graphs/networks, and text. For data stream mining, his main contribution is on capturing concept drift in real-time, while for graph/network mining, it is to exploit structures in the data or relationship, which can be probabilistic or evolving in nature, among the entities, where the network can consist of links and nodes of heterogeneous types. To better explore the availability of all kinds of data in the big data era, his more recent work has been on multi-source learning, which is on fusion of data from multiple sources, including multi-view and multimodality data, where the work has found many applications including social networks, e-commerce, health and brain informatics, and smart city, etc.
Dr. Yu received many prestigious awards, including 2013 IEEE Computer Society Technical Achievement Award "for pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data", and 2003 IEEE ICDM Research Contributions Award for his pioneering contributions to the field of data mining. His works also received the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014).
Dr. Yu is a Fellow of the ACM and the IEEE. He is currently the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004).
Dr. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University.
The previous SIGKDD Innovation Award winners have been: Rakesh Agrawal, Jerome Friedman, Heikki Mannila, Jiawei Han, Leo Breiman, Ramakrishnan Srikant, Usama M. Fayyad, Raghu Ramakrishnan, Padhraic Smyth, Christos Faloutsos, J. Ross Quinlan, Vipin Kumar, Jon Kleinberg, Pedro Domingos, and Hans-Peter Kriegel.
The award includes a plaque and a check for $2,500 and will be presented at the Opening Plenary Session of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2016), on Sunday, August 14th in San Francisco. Dr. Yu will present the Innovation Award Lecture immediately after the awards presentations.