Terry S. Yoo


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Most Recent Affiliation(s):


  • University of North Carolina at Chapel Hill, Senior Research Associate

Other / Past Affiliation(s):


  • University of Mississippi Medical Center
  • US National Library of Medicine

Bio:

  • SIGGRAPH 1994

    Terry S. Yoo is a research assistant in the computer science department at the University of North Carolina at Chapel Hill. His most recent position there was as site coordinator for the NSF/ARPA Science and Technology Center for Computer Graphics and Scientific Visualization. His research interests include medical image processing and interactive display of volumetric information. Prior to his appointment at UNC, he worked as a systems programmer for MCNC, as an Information Systems Designer for AT&T Technologies/Bell Labs and earlier as a member for the SIMNET project for BBN Laboratories Incorporated He received a BA in Biology from Harvard University in 198S, and an MS in Computer Science from the University of North Carolina at Chapel Hill in 1990. He is currently working on his doctoral degree in the segmentation and classification of MR images using a combined structural and statistical approach.  

    SIGGRAPH 1993

    Terry S. Yoo is a Senior Research Associate in the computer science department at the University of North Carolina at Chapel Hill. He is also a site coordinator for the NSF/DARPA Science and Technology Center for Computer Graphics and Scientific Visualization. His research interests include medical image processing and interactive display of volumetric information. Prior to his appointment at UNC he worked as a systems programmer for MCNC, as an Information Systems Designer on a data-flow signal processing machine for AT&T Technologies/Bell Labs and earlier as a member of the SIMNET project for BBN Laboratories Incorporated. He received a BA in Biology from Harvard University in 1985 and a MS in Computer Science from the University of North Carolina at Chapel Hill in 1990. He is currently working on his doctoral degree in the segmentation and classification of MR images using a combined structural and statistical approach.


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