Myung J. Lee


Main Affiliation

Electrical Engineering

Additional Departments/Affiliated Programs

Computer Engineering


Steinman Hall


ST 677


(212) 650-7260


(212) 650-8249

Myung Lee

Myung J. Lee


Dr. Myung J. Lee received a B.S and an MS from Seoul National University in Korea and Ph.D degree from Columbia University in electrical engineering. He is currently a professor at the Dept of Electrical Engineering and a doctoral faculty of computer science at Graduate Center of City University of New York. He is also an adjunct professor at GIST.

Dr. Lee’s recent research interests include machine learning based mobile edge cloud computing, stochastic computing  applications for Neural Net and intrusion detection system, secure V2X communications, and COSMOS wireless testbed beyond 5G. His researches have been funded by government agencies and leading industries, including, NSF, ARL, Bellcore, Samsung and ETRI. He published extensively in those research areas more than 200 journal and conference papers, book chapters, and more than 25 U.S. and international patents, and numerous international standard contributions including IEEE 802.15 standards (chair for TG5 WPAN Mesh and TG8 Peer Aware Communications) and ZigBee. Dr. Lee’s research group developed the first NS-2 simulator for IEEE 802.15.4, a standard NS-2 distribution widely used for wireless sensor network researches. He co-received the three best paper awards and CUNY Performance Excellence Award.


Ph.D., Electrical Engineering, Columbia University, New York, 1990

M.S., Electrical Engineering, Seoul National University, Korea, 1978

B.S., Electrical Engineering, Seoul National University, Korea, 1976 


Courses Taught

5G Mobile Communication & IoT

Wireless Communications

Probability and Random Processes

Wireless sensor/ad hoc networks

Advanced wireless multimedia networks

Senior Design


Research Interests

Dr. Lee’s current research interests include Machine Learning based management of resources for mobile edge cloud and IoT, Secure V2X group communications, stochastic computing based Deep Neural Net and Intrusion Detection System, and NSF COSMOS wireless testbed for future wireless communications.

Recent projects:

The COSMOS testbed will cover one square mile in West Harlem, with City College to the north, Columbia University’s Morningside Heights campus to the south, the Hudson River to the west, and Apollo Theater to the east. This vibrant, densely populated neighborhood is seen as an ideal place to push the bandwidth and latency limits of 4G, and even fifth-generation wireless technology, or 5G, which carriers are starting to roll out in some cities now.  

The COSMOS architecture has a particular focus on ultra-high bandwidth and low latency wireless communication tightly coupled with edge cloud computing. The COSMOS testbed will be deployed in upper Manhattan and will consist of 40-50 advanced software-defined radio nodes along with fiber-optic front-haul and back-haul networks and edge and core cloud computing infrastructure. Researchers will be able to run experiments remotely on the COSMOS testbed by logging into a web-based portal which will provide various facilities for experiment execution, measurements, and data collection.


  • NSF IRNC: Testbed COSMOS Interconnecting Continents (COSMIC)

A Rutgers/Columbia/Arizona/CCNY team has been awarded  from NSF entitled "IRNC: Testbed: COSMOS Interconnecting Continents (COSM-IC)".  The CCNY PI Prof. Saadawi and Co-PI Prof. Myung Lee. The project is aimed at development of an international networking and wireless testbed by federating US research testbeds including COSMOS, ORBIT, FABRIC and PEERING with experimental facilities in Ireland, Greece, Brazil and Japan.  The federated international testbed will enable experimental research on a wide range of  optical, wireless, SDN networking, inter-domain routing and edge computing experiments at a global scale.


  • NSF STTR Phase I:  Stochastic Computing-based Host Intrusion Detection System

Stochastic Computing (SC) to provide hardware Host Intrusion Detection System (HIDS) security solution for resource limited IoT devices.  The proposed approach exploited two salient properties of SC: 1) consumption of less silicon area and power, 2)  both SC and HIDS are inherently probabilistic in nature. SC is generated probabilistically from conventional number system that can perform complex mathematical operation using simple hardware blocks. The proposed approach provides industrial drone with HIDS based on simple hardware together with  Machine Learning Neural Network Algorithm. Coupled with other security layers such as cryptography, access control etc., this light weight hardware solution provides strong security to the resource limited IoT devices. Embedded inside the device, this HIDS monitors the incoming packets for any malicious activity and offers the important layer of security defense.  


  • NSF JUNO2:  Resilient Cloud Designed Networks (RECN)

The objective of this joint research between CUNY and Kyutech Japan is to conduct between   foundational research on a resilient edge cloud designed network to achieve basic understanding of the underlying science for future RECN. This work will cover issues of security, heterogeneity, resource constraints and potential mobility of end devices/sensors. A backbone network will be implemented and diversity of access network technologies, availability/placement of computing resources and Quality of Service (QoS) requirements will be examined.


  • MSRDC:  Noise-Aware Low-Cost Low-Power Baseband DSP Hardware using Stochastic Computing

The proposed stochastic computing-based baseband DSP has several benefits: significant reduction in hardware and power cost; error tolerant features, and stochastic nature of SC matches well with likelihood-based communication and signal processing. The task focus on developing a series of high-performance stochastic accelerators for various baseband DSP algorithms such as synchronizer, correlation detector, and high-order FIR filter for resource constrained IoT devices.

Publications (Selected)

  • A. Qadeer, M. Lee, K. Tsukamoto, “Flow-level Dynamic Bandwidth Allocation in SDN-enabled Edge Cloud using Heuristic Reinforcement Learning,” 8th IEEE Conference, on Future Internet of Things and Cloud, August, 2021
  • Shumpei Shimokawa, Yuzo Taeknak, Kazuya Tsukamoto, Myung Lee,  “SDN based in-network two-staged video QoE estimation with measurement error correction for edge network,” IEEE Access Journal, Vol. 9, Feb, 23, 2021  
  • K. Ahmed, M. Hernandez, M. Lee, K. Tsukamoto, “End-to-end Security for Connected Vehicles,” Proc. of The 12-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2020), Canada, Sept, 2020
  • (Invited Paper) T. Saadawi, A. Kawaguchi, M. Lee, A. Mowshowitz, “ Secure Resilient Edge Cloud Designed Network”, Trans. IEICE, Vol. E103-B, No. 4, pp.291-301 April 2020
  • Kazi Ahmed and Myung Lee, “Secure Resource Allocation for  LTE-based V2X Service,”    IEEE Transaction on Vehicular Technology, Vol. 67, No. 12, pp. 11324-1133, Dec. 2018
  • Kazi Ahmed and Myung Lee, “Secure, LTE-based D2D for V2X Services,”  IEEE IoT Journal, Vol. 5, No. 5, October, 2018, pp. 3724-3732
  • A. Qadeer, M. Lee, K. Tsukamoto, “Real-time Multi-resource Allocation via Structured Policy Table,”  Proc. INCoS-2019, Sept, 2019
  • (Best Paper) S. Yamasaki, T. Kanaoka, Y. Taenaka, K. Tsukamoto, M. Lee, “ SDN-based time-domain error correction for in-network video QoE estimation in wireless networks,” Proc. of INCoS-2019, Sept. 2019
  • Kwang Myung Jeon, Chan Jun Chun, Hong Kook Kim, and Myung J. Lee, “User-Aware Audio Marker Using Low Frequency Ultrasonic Object Detection and Communication for Augmented Reality,”  Journal of Applied Science, Appl. Sci. May 2019
  • K. Ahmed, B. Yuan, M. Lee, “High Accuracy Stochastic Computing based FIR Filter Design,” ICASSP 2018, Canada 2018
  • M. Hussain, W. Brandauer, Myung Lee, “Mobility-aware Vehicle-to-Grid (V2G) Optimization for Uniform Utilization in Smart Grid based Power Distribution Network,” Journal of Mobile Networks and Application (MONET),  April, 2018
  • M Hussain and M. Lee, “End-to-end Delay minimization in Multi-Channel, TDMA Wireless Sensor Networks by Particle Swarm Optimization,” IEEE ICACCE 2018 (IEEE International Conference on Advances in Computer and Communication Engineering), Paris, 2018
  • Yanchen Liu and Myung Lee, Yangyang Zheng, “Multi-Resource Allocation for Cloudlet-Based Mobile Cloud Computing System,” IEEE Transaction on Mobile Computing, Vol. 15., No. 10, Oct. 2016
  • K. Jeon, H. Kim,  M. Lee, “Non-coherent Low-frequency Ultrasonic Communication System with Optimum Symbol Length,” International Journal of Distributed Sensor Networks,  May  2016
  • Y. Kim and M. Lee, “Scheduling Multi-channel and Multi-timeslot in Time Constrained Wireless Sensor Networks via Simulated Annealing and Particle Swarm Optimization,” IEEE Communications Magazine, Vol. 52. No. 1, January 2014. 
  • S. Yamasaki, D. Nobayashi, K. Tsukamoto, T. Ikenaga, M. Lee, “On-demand Transmission Interval Control Method for Spatio-Temporal Data Retention,”  The 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019). Japan, Sept. 2019
  • K. Jeon, C. Chun, H. Kim, and M. Lee , “ Application of Low-Frequency Utlrasonic Communication to Audio Marker for Augmented Reality, IEEE ICCE 2017.
  • (Best Paper) M. Hussain and M.  Lee, “Mobility Incorporated Vehicle-to-Grid (V2G) Optimization for Uniform Utilization in Smart Grid based Power Distribution Network,” 1st EAI International conference on Smart Grid Inspired Future Technologies, May 2016, Liverpool, UK
  • Kazhi Ahmed and Myung Lee, “Layered Scalable WAVE Security for VANET” IEEE Milcom 2015
  • Yanchen Liu and Myung Lee, “Security-aware resource allocation for mobile cloud computing systems,” 24th IEEE International Conference on Computer Communication and Networks, ICCCN 2015, Las Vegas, Aug. 2015.