Jung-Eun Kim

I am an assistant professor in Electrical Engineering & Computer Science (EECS) at Syracuse University. I am also a part of the Aging, Health, and Neuroscience (AHN) cluster. I am looking into AI/Machine learning through the lens of Systems. My research interests span Resource-/Time-dependent learning, AI/Machine learning for cyber-physical and embedded systems, Safety-/Time-critical systems, and Embedded multicore systems. Prior to joining EECS Syracuse, I was an associate research scientist in Computer Science at Yale University. I received my PhD degree 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, Korea.


Available Positions

I am looking for Phd/master/undergrad students who are highly motivated. Please reach out to me for potential research opportunities.


Anytime Learning: resource-/time-dependent learning

Based on considerations of performance-resource trade-offs of learning applications especially in cyber-physical and embedded platforms operating in resource-constrained environment, the framework is designed with adaptive concreteness to produce a feasible answer quickly and to produce an improved answer by performing additional processing as time permits, instead of enforcing strict or predefined requirements for resource/quality. Moreover, the resource limit when the solution is required may not be known far in advance, so whenever possible, the system can be ready at all times to produce the highest-quality solution it has found so far. Hence, it provides “ballpark” early results, and gradually higher-quality later results.


AnytimeNet: Controlling Time-Quality Trade-Offs in Deep Neural Network Architectures

Deeper neural networks, in particular (which obtain near-state-of-the-art performance with complex structures and a large number of internal parameters), require a massive amount of CPU/GPU processing capacity to obtain solutions with rigorously refined quality. Over the ranges of interest, there is a trade-off between solution quality and the resource required to produce the solution. It is tackled by constructing a deep neural network in a modular and adaptive way – the framework breaks down this complexity into smaller building blocks, so as to facilitate implementation and maintenance. An instance is shown with simplified ResNet blocks – in early iterations, fewer number of blocks are used while in later iterations more blocks are used to provide gradually better results.

ABC: Abstraction Before Concreteness

Under the same umbrella and philosophy in mind with AnytimeNet above, for resource-scarce environment, neural network is designed for adaptive concreteness thru data hierarchy: learning abstract information earlyconcrete information later.  For example, as shown in the figure, recognizing a category that contains a “stop” sign (i.e., urgent signs) is more time-critical than one containing “speed limit” signs. This is because a stop sign requires early action in a timely manner. The intermediate results can be utilized to prepare for early decisions/actions as needed. To apply this framework to a classification task for example, the datasets are categorized in an abstraction hierarchy. Then the framework classifies intermediate labels from the most abstract level to the most concrete.


Selected Honors and Awards

  • 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, 2020–2023
  • GPU Grant by NVIDIA Corporation, 2018
  • The MIT EECS Rising Stars, 2015
  • The Richard T. Cheng Endowed Fellowship, 2015 – 2016



  • Intelligent cyber-physical system, Fall 2021


Conference / Journal Publication

Workshop Publications, Technical Reports, Dissertation

Presentations and Talks

  • Department of Computer Science at Yale University, lecture of Self-Driving Cars: Theory and Practice (CPSC 235/EENG 245), “Time-Dependent Machine Learning, to be incorporated in CPS,” Apr. 22, 2019.
  • Department of Computer Science at the University of Illinois at Urbana-Champaign, lecture of Embedded Systems (CS 431), “Decision-Centric Data Scheduling in Emerging Cyber-Physical Systems I & II,” Apr. 19 & 24, 2018.
  • Department of Computer Science at the University of Illinois at Urbana-Champaign, lecture of Modern Real-Time Systems (CS 598), “Decision-Centric Data Scheduling in Emerging Cyber-Physical Systems,” Nov. 8, 2017.
  • U.S. Army Research Laboratory, Adelphi, MD, “Decision-Centric Data Scheduling for Autonomous Cyber-Physical Systems,” Nov. 1, 2017.
  • The 20th ACM/IEEE Design, Automation, and Test in Europe (DATE 2017), “A Schedulability Test for Software Migration on Multicore Systems,” Mar. 30, 2017.
  • The 37th IEEE Real-Time Systems Symposium (RTSS 2016), “Sporadic Decision-centric Data Scheduling with Normally-off Sensors,” Dec. 1, 2016.
  • Rockwell Collins Inc., Community of Practice meeting, “Sporadic Decision-centric Data Scheduling with Normally-off Sensors,” Nov. 17, 2016.
  • The 22nd IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, “On Maximizing Quality of Information for the Internet of Things: A Real-time Scheduling Perspective (Invited paper),” Aug. 19, 2016.
  • The MIT EECS Rising Stars 2015, “A New Real-Time Scheduling Paradigm for Safety-Critical Multicore Systems” Nov. 9, 2015.
  • The 21st IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2015), “Budgeted Generalized Rate Monotonic Analysis for the Partitioned, Globally Scheduled Uniprocessor Model,” Apr. 16, 2015.
  • The 18th ACM/IEEE Design, Automation, and Test in Europe (DATE 2015), “Schedulability Bound for Integrated Modular Avionics Partitions,” Mar. 10, 2015.
  • Rockwell Collins Inc., Community of Practice meeting, “Integrated Modular Avionics (IMA) Partition Scheduling with Conflict-Free I/O for Multicore Avionics Systems,” Sep. 30, 2014.
  • The 38th IEEE Computer Software and Applications Conference, “Integrated Modular Avionics (IMA) Partition Scheduling with Conflict-Free I/O for Multicore Avionics Systems,” Jul. 23, 2014.
  • The 16th ACM/IEEE Design, Automation, and Test in Europe (DATE 2013), “Optimized Scheduling of Multi-IMA Partitions with Exclusive Region for Synchronized Real- Time Multi-Core System,” Mar. 20, 2013.
  • Rockwell Collins Inc., “Single Core Equivalent Configuration for Multicore Avionic Systems,” Aug. 15, 2012.
  • The 32th IEEE Real-Time Systems Symposium (RTSS 2011), “Optimizing Tunable WCET with Shared Resource Allocation and Arbitration in Hard Real-Time Multicore Systems,” Dec. 2, 2011.


  • 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.