Hafeez Ullah Amin

Profile

Biography

Hafeez is currently working as a Senior Lecturer at the School of Computer Science, Edge Hill University, UK. He has been working as an Assistant Professor at University of Nottingham, Malaysia Campus from 2020 to 2023. He has over 12 years of teaching & research experience in various positions, such as Assistant Professor, Postdoctoral Researcher, Research Scientist, Teaching Assistant, and Data Analyst. He completed his Ph.D. from Universiti Teknologi PETRONAS (UTP), Malaysia with a major in Electrical and Electronic Engineering and a speciality in EEG Signal Processing with Machine Learning. Prior to joining Ph.D. studies, he received B.Sc (Hons) degree in Information Technology and M.S. degree in Computer Science with Artificial Intelligence in 2006 and 2009, respectively. For the last twelve years, he has been involved in multidisciplinary research including Biomedical Signal Processing, Data Analytics, Applied AI and Machine Learning. He has published over 50 articles in flagship international conference proceedings and peer-reviewed high impact factor journals with a total cumulative impact factor above 70. He is the co-author of a book on EEG experiment design and a granted patent on EEG Signals for long-term memory (LTM) assessment. His research expertise and interests include Neuroimaging, Biomedical Signals Processing, Neuroinformatics, and Applied Machine Learning. He is a Senior Member IEEE and Fellow (FHEA) of Advanced HE, UK.

Research Interests

PhD projects and Research Interests includes (but not limited to):

• EEG Signal Processing with Machine Learning
• AI Applications in Mental Healthcare
• Machine Learning Applications in Business Analytics  
• Neuroimaging and Health Data Analytics

Teaching

Programming for Data Science and AI (CIS4514, Module Leader)

Programming Principles and Techniques (CIS4504, Module Leader)

Data mining and Visualisation (CIS4519, Module Leader)

Machine Learning (CIS4513, Member)

• Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques
• Modulation of cortical activity in response to learning and long-term memory retrieval of 2D verses stereoscopic 3D educational contents
• A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques
• Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory