Jung-Eun Kim

I am an assistant professor in Computer Science at North Carolina State University. I work on trustworthy, interpretable, and efficient AI/machine learning. I am interested in bias and efficiency issues in deep learning. I also have a background in safety-critical 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 with regard to mine.


Research Focus

Somebody said, “If you think you are not biased, you are dangerous.” That is so true and not different for learning models. We understand a neural network model can be biased, and so we and our models would be safe.

In our group, we care about trustworthy, interpretable, efficient, and sustainable AI/deep learning. We fundamentally anatomize neural networks to understand and verify what is invariant in them and what causes biases and failure modes. Once we interpret all the anatomy of the architectures, why not use them efficiently and sustainably in the era of overwhelmingly large models? All of this is our mission, and we are savvy about them.


Media Coverage


Selected Honors and Awards

  • 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!
  • 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

Program Committee/Panel Service

  • Publicity Chair of IJCAI 2024
  • Senior Program Committee of AAAI 2024
  • Program Committee/Reviewer of ICML 2024, ICLR 2024, NeurIPS 2023-2024, IJCAI 2023-2024, AAAI 2023-2024
  • NSF review panel 2023 (for a different program from the other)
  • NSF review panel 2023
  • Technical Programme Committee of DAC 2023 -2024 for A1 AI/ML algorithms
  • Technical Programme Committee of DATE 2023-2024
  • 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
  • Jianwei Li
  • Rishi Singhal
  • Sai Kishore Honnavalli Ravi Shankar


  • Deep learning beyond accuracy, Fall 2023, Fall 2024
  • Resource-dependent neural networks, Spring 2023, Cloud GPU provided by Lambda, worth $17,280. Thank you, Lambda!
  • Resource-/Time-dependent learning, Fall 2022



(* Students that I advise(d) are underlined.)



  • 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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