Chang-hee (Andy) Won is a professor in the Department of Electrical and Computer Engineering and the director of Control, Sensor, Network, and Perception (CSNAP) Laboratory at Temple University. Previous to coming to academia, he worked at Electronics and Telecommunications Research Institute as a senior research engineer. Currently, he is actively guiding various research projects funded by the National Science Foundation, Pennsylvania Department of Health, and the Department of Defense. He published over 120 peer-reviewed articles and received multi-million dollars of research funding as a principal investigator from industry, state, and federal funding sources. He is a frequent reviewer for the National Science Foundation review panels and sits on National Institute of Health Biomedical Imaging Study Section. His research interests include tactile sensing, optimal control theory, spectral imaging, sensors, and dynamic sensing systems.


Research Interests

  • Tactile Sensors
  • Optimal Control Theory
  • Spectral Imaging
  • Dynamic Sensing

Courses Taught




ECE 3412

Classical Control Systems


ECE 5022

Engineering Analysis and Applications


ECE 5412

Control System Analysis


ECE 8110

Special Topics: Tactile Sensing Systems & Scientific Writing


ENGR 5022

Engineering Analysis and Applications


Selected Publications

  • Oleksyuk, V., Rahman, N., & Won, C. (2023). Tactile Sensing System and Convolutional Neural Network for Mechanical Property Classification. IEEE Sensors Letters, 7(10), pp. 1-4. Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/lsens.2023.3310356

  • Ahn, H. (2023). Editorial: The 22nd International Conference on Control, Automation, and Systems (ICCAS 2022). International Journal of Control, Automation and Systems, 21(8), pp. 2429-2429. Springer Science and Business Media LLC. doi: 10.1007/s12555-023-9901-0

  • Kim, C., Park, E., Won, C., & Lee, J. (2023). Remote Bio Vision: Perfusion Imaging Based Non-Contact Autonomic Biosignal Measurement Algorithm. IEEE Sensors Journal, 23(14), pp. 16324-16331. Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/jsen.2023.3279328

  • Rahman, N. & Won, C. (2022). Identifying Benign and Malignant Breast Tumor Using Vibro-acoustic Tactile Imaging Sensor. 2022 IEEE Sensors. 2022 IEEE Sensors: IEEE. doi: 10.1109/sensors52175.2022.9967083

  • Choi, S.I.n., Kim, A., & Won, C. (2022). Tissue Viscoelasticity Quantification Using Smartphone Tactile Imaging Probe With an Indenter and Viscoelastic Pitting Recovery Model. IEEE Sensors Journal, 22(15), pp. 15365-15372. Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/jsen.2022.3185009

  • Kim, C., Hwang, S., Park, E., Won, C., & Lee, J. (2021). Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks. Sensors (Basel), 21(16). Switzerland. doi: 10.3390/s21165551

  • Won, C.H., Lee, J.H., & Saleheen, F. (2021). Tactile Sensing Systems for Tumor Characterization: A Review. IEEE Sensors Journal, 21(11), pp. 12578-12588. doi: 10.1109/JSEN.2021.3078369

  • Choi, S., Oleksyuk, V., Caroline, D., Pascarella, S., Kendzierski, R., & Won, C. (2020). Breast Tumor Malignancy Classification using Smartphone Compression-induced Sensing System and Deformation Index Ratio. Annu Int Conf IEEE Eng Med Biol Soc, 2020, pp. 6082-6085. United States. doi: 10.1109/EMBC44109.2020.9176636

  • Saleheen, F. & Won, C.H. (2019). Statistical Stackelberg game control: Open-loop minimal cost variance case. Automatica, 101, pp. 338-344. doi: 10.1016/j.automatica.2018.12.015