Jung-Eun Kim

I am an assistant professor in Computer Science at North Carolina State University. I am looking into AI/deep learning through the lens of systems. I work on trustworthy and efficient deep learning.  I also have a background in safety-/time-critical systems, and cyber-physical and embedded systems. Prior to joining NC State in 2022, I was an assistant professor in EECS at Syracuse University (2021-2022.) Before then, I was an associate research scientist in Computer Science at Yale University. I received my PhD in Computer Science at the University of Illinois at Urbana-Champaign, and my BS and MS degrees in Computer Science and Engineering at Seoul National University, Seoul, Korea.



Available Positions

I am looking for genuinely motivated PhD students. If you are interested in working with me for a PhD program, please contact me with your CV and mention your research interests and experiences.


Media Coverage


Research Focuses

I am interested in deep learning and neural network architectures with bias and resource considerations. I am exploring bias issues arising in deep learning which usually arise due to a discrepancy between train and test sets. I am also interested in exploring trade-offs between resource and performance or other factors/aspects/dimensions/functions/values we have not been explicitly aware of. For instance, that is to see what we might lose/gain when we make a machine learning model more compact (by such as pruning, knowledge distillation, or quantization, etc.) (Our NeurIPS ’22 paper is an instance – Featured in Spotlight and News coverage). Also, I am interested in developing a model that “keeps learning” for autonomy so that it does not stay at a statically trained model. When the learning continues, the data itself, the environment, or the mapping of the two may change and get less certain. Overall, I care to make a model trustworthy, efficient, and sustainable.


Selected Honors and Awards

  • IBM workforce development funds, 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!
  • 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


  • Senior Program Committee of AAAI 2024
  • Reviewer on ICLR 2024
  • NSF review panel 2023 (for a different program from the other)
  • NSF review panel 2023
  • Reviewer on NeurIPS 2023
  • Program Committee of IJCAI 2023
  • Program Committee of AAAI 2023
  • Technical Programme Committee of DAC 2023 for A1 AI/ML algorithms
  • Technical Programme Committee of DATE 2023, DATE 2024 for E3 Machine learning solutions for embedded and cyber-physical systems
  • Web Chair of CPS-IoT Week 2023
  • Program Committee of Programming and System Software of IEEE Cluster 2023



I am fortunate to advise and work with the brilliant students who have a vision for the future:

  • Xingli Fang
  • Varun Mulchandani
  • Vishwesh Sangarya


  • Deep learning beyond accuracy, Fall 2023
  • Resource-dependent neural networks, Spring 2023, Cloud GPU provided by Lambda, worth $17,280. Thank you, Lambda!
  • Resource-/Time-dependent learning, Fall 2022
  • Intelligent cyber-physical system, Fall 2021


Conference / Journal Publication

Workshop Publications, Technical Reports, Dissertation


  • Chang-Gun Lee, Jung-Eun Kim, and Junghee Han. Sensor Deployment System for 3-Coverage. KR 10-1032998, filed Dec. 30, 2008, and issued Apr. 27, 2011.

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