Jinoh Kim, Ph.D.
Associate Professor

  • Faculty
Computer Science & Information Systems
Contact
Office
Journalism 217
Related Department
Computer Science and Information Systems

Educational Background

  • Ph.D., Computer Science, University of Minnesota, 2010
  • MS, Computer Science, Inha University, South Korea, 1994
  • BE, Computer Science and Engineering, Inha University, South Korea, 1991

Academic Positions

  • Assistant Professor, Texas A&M University-Commerce, 2012-present
  • Assistant Professor, Lock Haven University of Pennsylvania, 2011-2012

Research Interests

  • Data-intensive computing, distributed systems, cloud/grid computing
  • Network traffic measurement and analysis, network monitoring and security
  • Big-data computing and infrastructure, bit-data analytics, big-data applications

Professional Organizations

  • Association for Computer Machinery
  • Institute of Electrical and Electronics Engineers
  • Society of Design and Process Science
  • Korean-American Scientists and Engineers Association

Research Funding

  • $50,000, ETRI, 2017
  • $48,000, ETRI, 2016-2017
  • $193,723, Sysmate Inc., 2012-2015
  • $122,000, ETRI, 2013-2015

Selected Publications

  • Muthahar Syed, Jinoh Kim, and Taehyun Hwang,“Scalable Gene Sequence Analysis using Big Data Computing Technologies,” (Editor: Sang C. Suh), Springer, in press
  • Jon Weissman and Jinoh Kim, “Network Awareness for Volunteer Networks,” in Desktop Grid Computing (Editor: Christophe Cerin and Gilles Fedak), Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series, ISBN-10: 1439862141, ISBN-13: 978-1439862148, June 2012
  • Jerry Chou, Jinoh Kim, and Doron Rotem, “Energy Saving Techniques for Disk Storage Systems,” in Handbook of Energy-Aware and Green Computing (Editor: Sanjay Ranka and Ishfaq Ahmad), Chapman & Hall/CRC Computer & Information Science Series, ISBN-10: 1466501162, ISBN-13: 978-1466501164, January 2012
  • Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, Kuinam J. Kim, “A Survey of Deep Learning-based Network Anomaly Detection,” Cluster Computing, Springer, in press (impact factor 1.51)
Navigate This Page