Seeing Beyond Vision: RF-Based Perception for Robust and Intelligent Sensing

ABSTRACT

Perception and understanding of the physical world are fundamental to a wide range of applications, from autonomous systems to smart healthcare and human-computer interaction. However, traditional vision-based sensing—such as cameras and LiDAR—struggles in adverse conditions, including darkness, fog, and occlusions. In this talk, I will discuss how radio frequency (RF) signals provide a powerful alternative by penetrating visual barriers while maintaining high-resolution imaging capabilities. I will present our latest research on RF-based perception systems, including high-resolution 3D RF imaging and RF-based SLAM for large-scale mapping. Our work leverages advanced signal processing and machine learning to bridge the gap between RF perception and optical sensing. I will conclude with a discussion on the future of RF-based sensing in autonomous vehicles, smart healthcare, and cyber-physical systems.

BIOGRAPHY

Dr. Mingmin Zhao is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, where he leads the Wireless, Acoustic, Vision & Embedded Sensing (WAVES) Lab. His research focuses on novel sensing modalities that leverage radio frequency (RF) signals and machine learning to enable robust perception in challenging environments. He is a recipient of the ACM SIGMOBILE Doctoral Dissertation Award Runner-up, the ACM SIGMOBILE Research Highlights, the CACM Research Highlights, the Baidu Fellowship, and the Yunfan Award for Rising Stars in AI. His research on contactless health monitoring has been adopted by the industry and deployed in major hospitals and patients' homes across the United States. He received his Ph.D. from the Electrical Engineering and Computer Science Department at MIT in 2021, and his B.S. in Computer Science from Peking University in 2015.