Jung-Eun Kim

Assistant Professor

Electrical Engineering and Computer Science

4-191 CST

jkim150@syr.edu

Degree(s):

Ph.D. Computer Science, University of Illinois at Urbana-Champaign

M.S. Computer Science and Engineering, Seoul National University, Seoul, Korea

B.S. Computer Science and Engineering, Seoul National University, Seoul, Korea

Research interests:

  • AI/Machine learning for Cyber-Physical Systems (CPS)
  • Safety-/Time-critical systems
  • Real-time embedded systems

Current Research:

Dr. Kim’s current research lies at cyber-physical systems incorporating AI/machine learning capabilities. Cyber-physical and embedded systems are usually running in a resource-constrained environment. Hence, depending on the criticality of the learning applications, an advanced learning performance could be compromised while satisfying certain temporal and spatial requirements of the system’s core and essential tasks. Her research interest also includes developing systematic and analytic way to enhance and/or decompose the performance of learning components running in cyber-physical and embedded systems.

Teaching Interests:

My teaching interests include cyber-physical systems for safety-critical systems and/or machine learning systems, as well as real-time embedded systems.

Honors:

  • NSF SaTC (Secure and Trustworthy Cyberspace): CORE: Small: Partition-Oblivious Real-Time Hierarchical Scheduling, Co-PI, National Science Foundation, 2020–2023
  • GPU Grant by NVIDIA Corporation, 2018
  • The MIT EECS Rising Stars, 2015
  • The Richard T. Cheng Endowed Fellowship, 2015 – 2016

Recent Publications:

  • Man-Ki Yoon, Mengqi Liu, Hao Chen, Jung-Eun Kim and Zhong Shao, “Blinder: Partition-Oblivious Hierarchical Scheduling,” in Proceedings of the 30th USENIX Security Symposium (USENIX Security ’21), Aug. 2021.
  • Jung-Eun Kim, Richard Bradford, Max Del Giudice and Zhong Shao, “Adaptive Generative Modeling in Resource-Constrained Environments,” in Proceedings of the 24th ACM/IEEE Design, Automation, and Test in Europe (DATE), Feb. 2021.
  • Jung-Eun Kim, Richard Bradford, Max Del Giudice and Zhong Shao, “Paired Training Framework for Time-Constrained Learning,” in Proceedings of the 24th ACM/IEEE Design, Automation, and Test in Europe (DATE), Feb. 2021.
  • Jung-Eun Kim, Richard Bradford and Zhong Shao, “AnytimeNet: Controlling Time-Quality Tradeoffs in Deep Neural Network Architectures,” in Proceedings of the 23rd ACM/IEEE Design, Automation, and Test in Europe (DATE), Mar. 2020.
  • Jung-Eun Kim, Richard Bradford, Man-Ki Yoon and Zhong Shao, “ABC: Abstract prediction Before Concreteness,” in Proceedings of the 23rd ACM/IEEE Design, Automation, and Test in Europe (DATE), Mar. 2020.