Giljae Lee

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Graduate Research Assistant

I am a Ph.D candidate under the supervision of Dr. José A.B. Fortes in the Department of Electrical and Computer Engineering at the University of Florida. Before joining the ACIS team, I worked at the Korea Institute of Science and Technology Information (KISTI) supercomputing center in South Korea as a Network Researcher, where I performed research on grid computing, high performance networking and sensor networking. I received my M.S. degree at the University of Florida in 2012. My research interests include cloud/distributed computing, autonomic computing, and software-defined system.

Currently I'm involved in the PRAGMA-ENT project. My aims in the project are the following:

  • Integrate virtual cluster based computing infrastructure with highly dynamic Linux container based technology (such as Docker).
  • Develop autonomic approach to optimize resource allocation and assignment in PRAGMA-ENT environment.

PRAGMA-ENT: Enabling Scientific Expeditions and Infrastructure Experimentation for Pacific Rim Institutions and Researchers

  • J. Kon, G. Lee, J. A. B. Fortes, and S. Yamaguchi, “A Kernel-based Method for Extracting the Performance Issue of Secure Frequent-pattern Mining with FHE”, (under review)
  • G. Lee and J. A. B. Fortes, “Improving data-analytics performance via autonomic control of concurrency and resource units”, (under revision)
  • K. Nakashima, J. Kon, G. Lee, J. Fortes, and S. Yamaguchi, “A Study on Big Data I/O Performance with Modern Storage Systems”, in IEEE International Conference on Big Data, Boston, MA, USA, 2017.
  • G. Lee and J. A. B. Fortes, “Hierarchical Self-Tuning of Concurrency and Resource Units in Data-Analytics Frameworks”, in IEEE International Conference on Autonomic Computing (ICAC), USA, 2017.(Best Student Paper Award)
  • G. Lee and J. A. B. Fortes, “Self-Tuning of Job Concurrency for Hadoop Performance Improvement", in PRAGMA 32, Gainesville, USA, 2017. (Best Poster Award Nominee)
  • G. Lee and J. A. B. Fortes, “Hadoop Performance Self-Tuning Using a Fuzzy-Prediction Approach”, in IEEE International Conference on Autonomic Computing (ICAC), Germany, 2016.

DARPA REPAIR: Creating the Synthetic Brain Through Hybrid Computational and Biological Systems: Repairing and Replacing Neural Networks

  • G. Lee, et al., “Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models”, Journal of Computational Surgery, vol. 3, no. 12, 2014.
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