As a key enabler for 6G, ISAC merges radar and communication functionalities into a unified system that shares both hardware and spectral resources. To this end, we develop advanced and secure ISAC systems through: distributed and networked ISAC architectures, novel signaling and signal processing design,  antenna array optimization to balance sensing and communication performance.  In our lab, the work goes beyond theoretical analysis. Hardware prototypes will be developed to demonstrate system feasibility and validate performance under practical constraints. This opens opportunities to create new ISAC frameworks that explicitly consider non-ideality, hardware limitations, and implementation feasibility, enabling the transition from concept to deployable 6G technologies.

Advances in millimeter-wave technologies have accelerated the adoption of imaging radar (or ISAC) in applications such as autonomous vehicles, UAVs, and robotics. However, key challenges remain in achieving high-resolution, interference resilience, and cost-effectiveness. This research addresses these issues through advanced signal processing, hardware-efficient system design, and antenna array configurations. Connection to ISAC frameworks and digitally modulated waveforms will further enable collaborative sensing and interference management. Low-power and hardware-aware solutions tailored for dynamic and cluttered environments allow high-resolution imaging to be applied in physical environment awareness, which further enables to build physical AI aided by wireless networked sensing.

Wireless RF sensing is rapidly advancing as a key technology for extracting human physiological signals in a fully contactless manner, enabling new possibilities in biomedical monitoring and diagnostics. Our research group develops RF system architectures and motion-robust signal processing algorithms that enhance radar and ISAC capabilities, allowing precise detection of cardiopulmonary motion, micro-vibrations, and other vital physiological indicators even in dynamic or cluttered environments. By leveraging adaptive waveform design, robust clutter suppression, and learning-based estimation techniques, we aim to overcome longstanding challenges posed by subject movement and multipath interference. Such advances not only improve the reliability and accuracy of contactless vital-sign monitoring but also accelerate the integration of RF sensing into pervasive healthcare, smart environments, and human-centered biomedical applications.