Biography
Ali Mohammad Alqudah is a Ph.D. student and Graduate Research Assistant in the Biomedical Engineering Program at the University of Manitoba, Canada. His research focuses on advancing healthcare solutions through artificial intelligence, particularly in biomedical signal processing and clinical applications. His current work involves developing novel methods for detecting Obstructive Sleep Apnea (OSA) during wakefulness using tracheal breathing sounds, leveraging machine learning and deep learning techniques at the Biomedical Instrumentation and Signal Analysis Lab.
He holds a B.S.E. in Biomedical Systems Engineering (2015) and an M.Sc. in Computer Engineering (Industrial Automation Engineering, 2018) from Yarmouk University, Irbid, Jordan. Prior to joining the University of Manitoba, he served as a Lab Engineer, Teaching & Research Assistant, and Lecturer in the Department of Biomedical Systems and Informatics Engineering at Yarmouk University from 2015 to 2022.
As an active contributor to the academic community, he serves as a reviewer for several prestigious peer-reviewed journals, including:
Medical & Biological Engineering & Computing
IEEE Journal of Biomedical and Health Informatics
IEEE Sensors Journal
IEEE Access
He is a member of multiple professional organizations, including:
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Engineering in Medicine and Biology Society (EMBS)
Association for Computing Machinery (ACM)
Jordan Engineering Association (JEA)
Institute for Systems and Technologies of Information, Control and Communication (INSTICC)
International Association of Engineers (IAENG)
Canadian Medical and Biological Engineering Society (CMBES)
International Information and Engineering Technology Association (IIETA)
His research interests span:
Artificial intelligence in healthcare
Pattern recognition
Biomedical signal and image processing
Committed to driving innovation at the intersection of engineering and medicine, his work aims to develop advanced, AI-driven technologies to improve diagnostic accuracy and patient outcomes.