Dr. Saadullah Farooq Abbasi possesses an impressive track record with over six years of experience in both research and teaching at renowned international institutions. His unwavering dedication to the academic and research community drives him to make significant contributions. Dr. Abbasi's academic journey has traversed prestigious institutions worldwide, including Fudan University in China, the University of Glasgow, and the University of Birmingham in the United Kingdom, alongside National University of Technology (NUTECH), Riphah International University, and ISRA University in Pakistan. Currently, he holds the esteemed position of Research Fellow within the Department of Electrical and Electronic Engineering at the University of Birmingham.

In addition to his role as a Research Assistant at the University of Glasgow and an Assistant Professor at Riphah International University, Dr. Abbasi has exhibited remarkable leadership as the Co-Chair of the Engineering Department's Examination Committee. His academic pursuits have been marked by excellence, earning a PhD as a Fulbright scholar at Fudan University and a Master's degree with distinction from NUST, Pakistan.

Dr. Abbasi's research portfolio spans a multitude of disciplines, including neonatal sleep, cybersecurity, artificial intelligence, deep learning, and machine learning, with a particular focus on applications in neonatal sleep and brain maturation. His expertise shines in EEG signal classification and neonatal image classification, with a significant publication record, including over 20 research papers in prestigious journals and international conferences, boasting substantial impact and citations. He is an active peer reviewer for distinguished journals, actively shaping the landscape of research.

Looking ahead, Dr. Abbasi's research direction is set on the path of innovation, concentrating on the development of cutting-edge deep learning algorithms for EEG signal classification, primarily in the domain of healthcare, addressing critical issues such as seizure detection, sleep classification, neonatal brain and physical development, and the prevention of elderly falls.