CV

Research Fellow

• Leading research on wireless sensing using Wi-Fi, with a focus on the IEEE 802.11bf protocol and Wi-Fi-based radio frequency fingerprinting (RFFI) for device authentication and physical-layer security.

Research Associate

• Developed innovative wireless security solutions for Wi-Fi devices using advanced machine learning techniques as part of the HASC Future Communications Hub.
• Designed RFFI algorithms achieving over 95% identification accuracy in cross-environment, NLOS, and mobile conditions.
• Developed a noise-robust RFFI system using a diffusion model; published at WCNC 2025 and received the Best Workshop Paper Award.

Ph.D

• Device-Free Wireless Sensing

M.Sc

• Communication and Signal Processing

B.Eng.

• Electronic Engineering

Domain Generalisable Wi-Fi-Based RFFI

• Developed a domain generalisable RFFI system capable of identifying Wi-Fi devices in previously unseen environments, addressing cross-environment robustness challenges caused by multipath propagation and channel-induced distortions.
• Demonstrated the system live at Mobile World Congress (MWC) 2026 Barcelona, processing over 52,000 inference packets in real-time and achieving 92% overall accuracy without prior exposure to the exhibition hall environment.
• Built a real-time prototype using USRP N210 SDR and multiple Wi-Fi dongles (9 devices across 3 brands) with deep learning-based device classification.

Teaching Assistant

• Assisted in teaching data-focused subjects (machine learning, statistics) as well as hardware-related topics (electronic circuit design, communication systems).

• WCNC 2025 Best Workshop Paper Award

• Live Demo at Mobile World Congress (MWC) 2026 Barcelona