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  •      Haibin Ling ()    (CV)
    Center for Data Analytics and Biomedical Informatics
    Dept. of Computer & Information Sciences, Temple University
    382 SERC Building, 1925 North 12th St., Philadelphia, PA 19122
    (tel)1-215-204-6973 | (fax)1-215-204-5082
    EMAIL: hbling AT | haibin.ling AT

    Short Bio: Haibin Ling received B.S. and M.S. from Peking University in 1997 and 2000, respectively, and Ph.D. from the University of Maryland in Computer Science in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at UCLA. He then worked for Siemens Corporate Research as a research scientist. In 2008, he joined Temple University where he is now an Associate Professor. Dr. Ling's research interests include computer vision, medical image analysis, visual privacy protection, and human computer interaction. Dr. Ling received the Best Student Paper Award of ACM UIST in 2003 and the NSF CAREER Award in 2014. He has served as a Guest Co-Editor for Pattern Recognition, and Area Chairs for CVPR 2014 and CVPR 2016.

    Student positions: I am looking for motivated students interested in computer vision, medical imaging, machine learning and related areas. Financial support will be provided to qualified Ph.D. students. If interested, please contact me at hbling AT
  • 2015-09: A paper on hand pose estimation accepted by ICCV'15.
  • 2015-06: Liang Du successfully defended his PhD thesis, congratulations!
  • 2015-06: Appointed as an Area Chair for CVPR'16.
  • 2015-04: A paper on nighttime traffic analysis accepted by IEEE Tran. on Intelligent Transportation Systems.
  • 2015-04: A paper on cross-age face recognition accepted by CVPR'15.
  • 2015-04: New grant for high-performance wide area motion imagery analysis.
  • 2014-09: Source code for the GPR-based tracker available: code, ECCV paper.
  • 2014-08: Received an NSF grant (co-PI) on surveillance using 3D camera and high speed network.
  • 2014-08: Received an NSF grant on osteoporosis prediction using dental data.
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