Qi Li
Qi Li 琪李

Assistant Professor

School of Electrical and Computer
University of Oklahoma

About Me

I am a tenure-track Assistant Professor at the School of Electrical and Computer Engineering, University of Oklahoma. I received my Ph.D from Colorado School of Mines under the supervision of Dr. Dong Chen.

My research interests lie at the intersection of Machine Learning, Image Processing, and Cyber-Physical Energy Systems. Recently, I have focused on integrating deep learning with image processing techniques to develop computer systems that enhance the energy efficiency of Cyber-Physical Systems (CPS), particularly in the contexts of smart homes, buildings, grids, and cities.
I am actively seeking highly motivated undergraduate and graduate researchers to join my lab! If you’re interested, please feel free to email me!
Recent News
• 09/24: VoiceAttack paper got accepted at ACM BuildSys’24!
• 08/24: Qi joined the School of Electrical and Computer Engineering at the University of Oklahoma!
• 07/24: We successfully organized the 2nd IoT security high school summer camp!
• 04/24: Qi’ PhD dissertation was nominated as Dr. Bhakta Rath and Sushama Rath Research Award!
• 04/24: Qi passed dissertation defense. Thanks to all the people who have helped and supported me through this wonderful Ph.D. journey!
• 04/24: Image Attack paper got accepted at ACM EWSN’24!
• 01/24: Qi was selected to participate in the 2024 CRA-WP Grad Cohort for Women.
Selected Publications

[IoTDI’23] SolarDetector: Automatic Solar PV Array Identification using Big Satellite Imagery Data. Qi Li, Sander Schott, and Dong Chen. In Proc. of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, May 9-12, 2023, San Antonio, Texas, part of CPS-IoT Week’23, Acceptance Rate = 30.27%.

[CNS’22] TrafficSpy: Disaggregating VPN-encrypted IoT Network Traffic for User Privacy Inference. Qi Li, Keyang Yu, Dong Chen, Mo Sha and Long Cheng. In Proc. of the 10th IEEE Conference on Communications and Network Security (CNS 2022), 3-5 October 2022, Austin, Texas, USA. Acceptance Rate = 35.25%.

[IPSN’21] SolarDetector: Automatic Solar PV Array Identification using Big Satellite Imagery Data. Qi Li, Sander Schott, and Dong Chen. In Proc. of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, May 9-12, 2023, San Antonio, Texas, part of CPS-IoT Week’23, Acceptance Rate = 30.27%.