题    目 (TITLE)：Exact Data Reduction for Big Data 

　　讲 座 人 (SPEAKER)：Associate Prof. Jieping Ye （University of Michigan）

　　主 持 人 (CHAIR)：程健 研究员

　　时    间 (TIME)：2015年7月23日（星期四）上午10:00

　　地    点 (VENUE)：中国科学院自动化研究所智能化大厦三层第一会议室　　

　　报告摘要（ABSTRACT）： 

　　Recent technological innovations have enabled data collection of unprecedented size and complexity. Examples include web text data, social media data, gene expression images, neuroimages, and genome-wide association study data. Such data have incredible potential to address complex scientific and societal questions, however analysis of these data poses major challenges for the scientists. As an emerging and powerful tool for analyzing massive collections of data, data reduction in terms of the number of variables and/or the number of samples has attracted tremendous attentions in the past few years, and has achieved great success in a broad range of applications. The intuition of data reduction is based on the observation that many real-world data with complex structures and billions of variables and/or samples can usually be well explained by a few most relevant explanatory features and/or samples. Most existing methods for data reduction are based on sampling or random projection, and the final model based on the reduced data is an approximation of the true (original) model. In this talk, I will present fundamentally different approaches for data reduction in that there is no approximation in the model, that is, the final model constructed from the reduced data is identical to the original model constructed from the complete data. Finally, I will use several real world examples to demonstrate the potential of exact data reduction for analyzing big data. 

　　报告人简介（BIOGRAPHY）： 

　　Jieping Ye is an Associate Professor of Department of Computational Medicine and Bioinformatics and Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He received his Ph.D. degree in Computer Science from the University of Minnesota, Twin Cities in 2005. His research interests include machine learning, data mining, and biomedical informatics. He has served as Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, ICDM, SDM, ACML, and PAKDD. He serves as PC Co-Chair of SDM 2015 and BioKDD 2015. He serves as Action/Associate Editor of Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper honorable mention in 2010, the KDD best research paper nomination in 2011 and 2012, the SDM best research paper runner up in 2013, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.