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Yinan Li
Ph.D. Candidate
Email: yinan926 at vt dot edu
Resume: [pdf] [doc]





Education
 
                                       GPA: 4.0/4.0
    

    M.S.                           Computer Science
                                       GPA: 3.9/4.0
    
    M.S. and B.S.          Computer Science, 
                       

Research Interest
  • Mobile computing, wireless networks, network security, performance modeling and optimization.
  • Distributed  and parallel computing, distributed systems.

Research Experiences
 Research Assistant                                        Jan. 2009 ~ present
 Mobile Computing Laboratory, Department of Computer Science, Virginia Tech

     Research Highlights
  • Integrated mobility and service management in wireless mesh networks
  • Cache consistency management for mobile data access in wireless mesh networks
  • Algorithms for mobile multicast services in wireless mesh networks

 Research Assistant                                        Aug. 2006 ~ Dec. 2008
 Innovative Computing Laboratory, Department of Computer Science, University of Tennessee, Knoxville

     Research Highlights
  • MAGMA
    • Model-driven auto-tuning framework for automatically generating highly-tuned dense linear algebra (DLA) kernels                           on parallel hybrid (CPU + GPU) many-core parallel architectures.
  • GridSolve
    • Complete interfaces to GridSolve in Octave and IDL.
    • Automatic workflow discovery and generation based on static code analysis, and workflow scheduling for efficient                 problem solving in GridSolve.

 Research Intern                                               May 2005 ~ Aug 2005

     Research Highlights
  • Web services matchmaking in Service-Oriented Architecture (SOA).
  • Eclipse plug-in that semantically translates model representations between XML and UML.
  • Eclipse plug-in that is capable of evaluating the similarity between two data models.
  
 Research Assistant                                        Sep. 2003 ~ Jun. 2006
 Grid Computing Laboratory

     Research Highlights
  • Campus wide Grid computing infrastructure.
  • Client tools for job submission, management, and status monitoring.
  • Peer-to-peer based resource sharing in Grid computing environments and supporting tools.