EECS500 Spring 2015 Department Colloquium

Jieping Ye
Sparse Learning: Models, Algorithms and Applications
University of Michigan
White 411
April 14, 2015

Sparse methods have been shown to be a versatile and powerful tool in many applications
including computer vision, image processing, genetics, and neuroscience. In this talk, we consider sparse methods for (1) variable selection where the structure over the features can be represented as an undirected graph or a collection of disjoint groups or a tree, (2) multi-source data fusion with a "blockwise" data missing pattern, (3) network construction, and (4) missing data estimation (matrix completion). We address the computational challenges by designing novel data reduction strategies which scale sparse methods to large-size problems.


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 a PC Co-Chair of SDM 2015. He serves as an 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.