Nidal Kamel, PhD
College of Engineering and Computer Science
Associate Professor, Electrical Engineering program
Biography
Assoc. Prof. Dr. Nidal Kamel is a faculty member in the Electrical Engineering Program within the College of Engineering and Computer Science (CECS) at VinUniversity. He is an internationally recognized expert in statistical signal and image processing, with more than three decades of research experience and a strong track record spanning estimation theory, stochastic modeling, higher-order statistics (HOS), principal component analysis (PCA) and subspace methods, filtering, signal and image/video processing for denoising and background initialization, visual motion magnification, telecommunications, neural networks, and artificial intelligence.
Dr. Kamel received his Master’s degree in 1989 and his PhD in 1994 in Statistical Signal Processing from the Technical University of Gdansk, Poland. Since 1994, he has led and contributed to numerous research projects grounded in rigorous theoretical foundations and advanced quantitative methodologies, establishing a sustained and impactful international research career.
He is currently a member of the Center of Environmental Intelligence at VinUniversity, where he leads research groups focused on the integration of remote sensing data—including multispectral and synthetic aperture radar (SAR) satellite imagery as well as UAV-based data—with deep learning models. His current work addresses high-impact applications such as carbon stock estimation, natural disaster monitoring, rice yield assessment, and air and water pollution analysis.
Prior to joining VinUniversity, Dr. Kamel served as an Associate Professor at Universiti Teknologi PETRONAS (UTP), Malaysia. During this period, his research primarily focused on the development of quantitative, data-driven methods for the assessment and understanding of brain disorders, including stress, anxiety, social anxiety disorder, epilepsy, and alcoholism. This translational research was conducted in close collaboration with hospitals and medical research institutions, employing multiple neuroimaging modalities such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS).
Assoc. Prof. Dr. Nidal Kamel is the author and co-author of more than 40 publications in leading international journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Image Processing, and IEEE Transactions on Circuits and Systems for Video Technology, in addition to publications with Elsevier, Springer, and other major academic publishers. He is also the author of two scholarly books on EEG analysis and source localization.
With his strong international academic background, sustained research impact, and clear interdisciplinary orientation, Assoc. Prof. Dr. Nidal Kamel continues to make significant contributions to VinUniversity’s research ecosystem, while mentoring and training the next generation of engineers and researchers to international academic standards.
• Satellite Image Processing for Environmental Monitoring
• Statistical Signal and Image Processing
• Estimation Theory and Stochastic Modeling
• Image/signal denoising and video background initialization
• Visual Motion Magnification
• Electroencephalography (EEG) for brain imaging
1. Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., & Chooi, W. T. (2015). Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian physical & engineering sciences in medicine, 38, 139-149.
2. Subhani, A. R., Mumtaz, W., Saad, M. N. B. M., Kamel, N., & Malik, A. S. (2017). Machine learning framework for the detection of mental stress at multiple levels. IEEE Access, 5, 13545-13556.
3. Jatoi, M. A., Kamel, N., Malik, A. S., Faye, I., & Begum, T. (2014). A survey of methods used for source localization using EEG signals. Biomedical Signal Processing and Control, 11, 42-52.
4. Awang, A., Husain, K., Kamel, N., & Aissa, S. (2017). Routing in vehicular ad-hoc networks: A survey on single-and cross-layer design techniques, and perspectives. IEEE Access, 5, 9497-9517.
5. Nidal, K., & Malik, A. S. (Eds.). (2014). EEG/ERP analysis: methods and applications. Crc Press.
6. Jatoi, M. A., Kamel, N., Malik, A. S., & Faye, I. (2014). EEG based brain source localization comparison of sLORETA and eLORETA. Australasian physical & engineering sciences in medicine, 37, 713-721.
7. Almahasneh, H., Chooi, W. T., Kamel, N., & Malik, A. S. (2014). Deep in thought while driving: An EEG study on drivers’ cognitive distraction. Transportation research part F: traffic psychology and behaviour, 26, 218-226.
8. Amin, H. U., Malik, A. S., Kamel, N., Chooi, W. T., & Hussain, M. (2015). P300 correlates with learning & memory abilities and fluid intelligence. Journal of neuroengineering and rehabilitation, 12(1), 1-14.
9. Al-Ezzi, A., Kamel, N., Faye, I., & Gunaseli, E. (2020). Review of EEG, ERP, and brain connectivity estimators as predictive biomarkers of social anxiety disorder. Frontiers in psychology, 11, 730.
10. Kamel, N. S., Sayeed, S., & Ellis, G. A. (2008). Glove-based approach to online signature verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 1109-1113.
1994: PhD, Statistical Signal Processing, Technical University of Gdansk, Poland
1989: MS, Statistical Signal Processing, Technical University of Gdansk, Poland
