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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)

Website

Bio

Jung-Eun Kim is an Assistant Professor in the Department of Computer Science at NC State University. She conducts fundamental AI/machine learning research, especially on Trustworthy, Interpretable, and Efficient AI/deep learning. Her research interests lie at the intersection of failure modes/safety risks/vulnerabilities and efficiency of deep learning. Before joining NC State, she was an associate research scientist in Computer Science at Yale University. She received her PhD in Computer Science from the University of Illinois at Urbana-Champaign, and BS and MS degrees in Computer Science and Engineering from Seoul National University, Seoul, Korea.

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

Trustworthy, Interpretable, and Efficient AI/deep learning

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
  • ICLR Spotlight, 2025
  • IBM Faculty award, 2023
  • NeurIPS Spotlight and a nomination for Best Paper Award, 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