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College of Engineering
Computer Science

Jung-Eun Kim

JK
Jung-Eun Kim, a a woman with dark hair pulled back, wearing a dark blazer over a light top, looking directly at the camera with a serious expression. She is seated in what appears to be a patterned chair, against a textured background. The photo is black and white.

Assistant Professor

3298 Engineering Building II (EB2)

919-515-5535 Website

Bio

Jung-Eun Kim is an Assistant Professor in the Department of Computer Science at NC State University. Her research focuses on integrating artificial intelligence and machine learning into cyber-physical systems, with an emphasis on safety-critical and real-time embedded systems. She investigates resource- and time-aware AI/ML techniques that can be deployed on edge and embedded platforms, aiming to enhance the reliability and efficiency of these systems.

Before joining NC State in 2022, Kim held academic and research positions at Syracuse University and Yale University. She earned her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2017. She also holds M.S. and B.S. degrees in Computer Science and Engineering from Seoul National University.

Her work has been recognized with several honors, including an NVIDIA GPU Grant, selection for the MIT EECS Rising Stars program, and the Richard T. Cheng Endowed Fellowship. She is a co-principal investigator on a National Science Foundation Secure and Trustworthy Cyberspace (SaTC) CORE project.

Education

Ph.D. Computer Science University of Illinois at Urbana-Champaign 2017

M.S. Computer Science and Engineering Seoul National Unviersity, Seoul, Korea 2009

B.S. Computer Science and Engineering Seoul National Unviersity, Seoul, Korea 2007

Area(s) of Expertise

Artificial Intelligence and Intelligent Agents
Data Sciences and Analytics

Grants

Date: 10/01/22 - 3/31/24
Amount: $281,629.00
Funding Agencies: National Science Foundation (NSF)

Real-time hierarchical scheduling facilitates modular reasoning about the temporal behavior of real-time applications by isolating their potential misbehavior. However, conventional time-partitioning mechanisms fail to achieve strong temporal isolation from a security viewpoint; variations in execution timings can be perceived by others, enabling illegitimate information-flow between applications completely isolated from each other in the utilization of CPU time. This project develops algorithmic solutions that make real-time partitions oblivious of others��� varying temporal behaviors, achieving non-interference-based security among partitions. The proposed work will allow such systems to employ advanced hardware and software technologies to develop high-end, real-time applications in a secure manner.


View all grants

Groups

  • ICLR Spotlight, 2025
  • IBM Faculty award, 2023
  • CRA Early & Mid Career Mentoring Workshop, 2023
  • Cloud GPU provided by Lambda, worth $17,280, for my course, Resource-dependent neural networks, Spring 2023. Thank you, Lambda!
  • NeurIPS Spotlight and a nomination for Best Paper Award, 2022
  • CRA (Computing Research Association) Career Mentoring Workshop, 2022
  • NSF SaTC (Secure and Trustworthy Cyberspace): CORE: Small: Partition-Oblivious Real-Time Hierarchical Scheduling, Co-PI, National Science Foundation, 2020–2024
  • GPU Grant by NVIDIA Corporation, 2018
  • The MIT EECS Rising Stars, 2015
  • The Richard T. Cheng Endowed Fellowship, 2015 – 2016