Dr Jinane Mounsef is on the advisory board of New York Institute and Laboratory for Artificial Intelligence (NYILAI) in the United States. Dr. Jinane Mounsef is a Ph.D holder in the Electrical Engineering-Focus area in Artificial Intelligence (AI) with a successful track record in higher education and an extended research experience spanning over more than 15 years, during which she worked with multidisciplinary teams on a long track of research papers and peer-reviewed publications in image processing, computer vision, and machine learning. She received the B.E. degree in computer and communications engineering from Notre Dame University, the M.S. degree in computer and communications engineering from the American University of Beirut, and the Ph.D. degree in electrical engineering from Arizona State University in the United States, where she was an invited member of the Honor Society of Phi Kappa Phi. She was recognized for her high academic achievements by being the recipient of the Charli Korban Award, the LibanCell Research Grant, and the Fellowship Scholarship for doctoral students. Dr. Mounsef served as a full-time faculty in the Electrical Engineering department at the American University in Dubai for seven years. She joined Rochester Institute of Technology (RIT) – Dubai in 2018 as an assistant professor in the Department of Electrical Engineering, where she leads the AI/Robotics Laboratory and plays a key role in the management of multidisciplinary academic, industrial and government projects to identify the next generation of safe and effective use of smart cities. She has widely participated in national and international seminars as a keynote and invited speaker. She is currently leading various smart applications, including healthcare (telemedicine, medical translator “Mosa’ed”, Covid Band), social inclusion (AI-Vision smart glasses), smart cities (anti-drowning system, smart pipeline leak detector, autonomous vehicles), and recognition of adversarial attacks (child protection in online gaming, facial recognition, network intrusion detection).