Megha Quamara

Dr. Megha Quamara is a Research Associate in the Department of Informatics at King's College London within the Faculty of Natural, Mathematical & Engineering Sciences. She actively contributes to the Horizon EU-funded SERMAS project under Prof. Luca Vigano's research group, focusing on security analysis of socio-technical systems integrating Extended Reality (XR) and human users.

Her educational background includes a PhD in Computer Science and Telecommunications from University of Toulouse III – Paul Sabatier (2019-2022), a Master of Technology in Cyber Security from National Institute of Technology Kurukshetra (2016-2018), and a Bachelor of Technology in Computer Science and Engineering (2011-2015).

Research interests center on Dependable systems engineering, Safe AI, Cybersecurity, and Formal methods with specialized applications in XR systems. Her work emphasizes formal-based automated tools for security analysis, user interaction modeling, and modular architecture design to address safety-security interplay in human-technology systems.

Recent publications (2024-2025) reveal a cohesive research trajectory in Extended Reality, examining social acceptance of XR agents, multi-modal behavior analysis, formal security verification of identification protocols, and modular system architectures. These studies collectively advance secure and user-centric XR deployment through interdisciplinary methodologies.

No scientific awards were documented in the provided materials.

Megha serves as Deputy Secretary of IEEE ComSoc's Women in Communications Engineering committee, promoting STEM diversity. Her grant involvement includes the Horizon EU SERMAS project and a Defence Research and Development Organization (DRDO) initiative during her master's. No advisees are listed in current records.

She operates within Prof. Luca Vigano's research group at King's College London, collaborating on the SERMAS project to establish foundational frameworks for next-generation XR systems through empirical studies and formal modeling approaches.