I am Mohammed Mosuily, an Assistant Professor in the Department of Computer Engineering at the College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University. My work spans artificial intelligence, signal processing, and digital health. I teach undergraduate courses in Natural Language Processing (NLP) and AI Fundamentals and Tools.
I have passed my PhD viva in Computer Science at the University of Southampton with modest corrections and am completing the corrections required before the degree is formally awarded. My doctoral research was conducted within the School of Electronics and Computer Science (ECS), as part of the Digital Health & Biomedical Engineering Group (DHBE), under the supervision of Dr Jagmohan Chauhan and Professor Paul White. My research explores how acoustic sensing and machine learning can support respiratory healthcare and wellbeing, particularly through accessible approaches to monitoring health beyond clinical settings.
I hold an MSc in Artificial Intelligence from the University of Southampton, where my project focused on using natural language processing to detect fake news on Twitter. I also hold a BSc in Electrical and Computer Engineering from King Abdulaziz University. Together, these experiences have shaped my interests across engineering, intelligent systems, and the analysis of audio and language.
My research interests include signal processing, generative artificial intelligence, large language models, digital health, and natural language processing. I am particularly interested in combining audio signal processing and machine learning to understand health-related signals and develop practical tools for healthcare and wellbeing.
My research projects include MMLung, which investigates smartphone-based lung-function estimation using audio and respiratory tasks, and EchoMotion, which explores acoustic sensing for exercise analysis and three-dimensional pose estimation. These projects have contributed to publications at INTERSPEECH 2023 and EUSIPCO 2025, respectively.
Before pursuing my PhD, I worked as a mobile application developer, gaining practical experience in software development and building mobile applications. I later worked as a Teaching Assistant, supporting laboratory sessions in courses including Advanced Programming in Java and Fundamentals of Digital Hardware. This combination of software development, engineering, and academic experience continues to inform my approach to research and teaching.
Through my teaching, I aim to help students connect fundamental concepts with practical applications and develop the skills needed to investigate real-world problems using AI. Through my research, I seek to contribute to accessible technologies that can make a meaningful difference to people’s health and everyday lives. I welcome opportunities to collaborate with researchers, students, and industry partners working in artificial intelligence, signal processing, digital health, and natural language processing.
