Title: Learning to Hash for Large Scale SearchSpeaker: Dr. Jun Wang (IBM T.J. Watson Research Center)http://www.ee.columbia.edu/~jwang/Time: 7 月 5 日（周四）下午 2:00-3:30Venue: 计算机楼 305Contact: 姜育刚 ygj@fudan.edu.cnAbstract:Fueled by the ever-increasing amount of data from the web, including images and videos, there is an emerging need to develop efficient search systems. However, many search tasks, such as visual content retrieval, have a nontrivial gap between theory and practice due to various critical factors, such as semantic gap and uncontrolled labeling. Realizing these challenges, we have leveraged machine learning methods to develop various hashing approaches for efficient indexing and search. In particular, I will mainly focus on our recent work about learning to hash, ranging from unsupervised to semi-supervised to supervised hashing function design.Speaker Bio:Jun Wang received his B.S. degree from Shanghai Jiao Tong University, M.S. degree from Tsinghua University, and M.Phil. and Ph.D. degrees from Columbia University in the City of New York, respectively. Currently, he is a Research Staff Member in the business analytics and mathematical sciences department at IBM T. J. Watson Research Center, Yorktown Heights, NY, USA. He also worked as a research intern at Google New York in 2009, and as a research assistant at Harvard Medical School, Harvard University in 2006. He has been the recipient of several awards and scholarships, including the Jury thesis award from the Department of Electrical Engineering at Columbia University in 2011, the IBM T. J. Watson Emerging Leader in Multimedia Award 2009, the Google global intern scholarship in 2009, and a Chinese government scholarship for outstanding self-financed students abroad in 2009. His research interests include machine learning, business analytics, information retrieval and hybrid neural-computer vision systems.
