Mahdi is a Lecturer in the School of Computing and Information Science at the Anglia Ruskin University. Mahdi's primary teaching focus is computer vision, artificial intelligence, and machine learning, Mahdi is also involved with modules on software engineering, computer networks, and electronics. Mahdi's research interests include deep learning and convolutional neural networks, machine learning, crowd analysis, and medical image processing.
Following his B.E in software Engineering at AZAD University in Iran, Mahdi moved to Malaysia to pursue postgraduate studies in the field of computer science. Mahdi obtained an M.Sc and then Ph.D in computer science from University Technology Malaysia in 2016. Mahdi's research at University Technology Malaysia was mainly focused on computer vision and machine learning. As a result of this research, several articles in various international journals and conferences have been published. After Mahdi completed his Ph.D. in 2016, he worked at University Technology Malaysia as a postdoctoral researcher, involved in a couple of projects sponsored by Cyber Security Malaysia, mainly aimed to promote safety and security in cyberspace using artificial intelligence. Following his postdoctoral position at University Technology Malaysia, Mahdi joined Kingston University London as a researcher to work on H2020 MONICA project, aimed to promote the crowd safety and security in large scale outdoor events using video analytics, artificial intelligence, and computer vision techniques. Mahdi joined Anglia Ruskin University in 2019, as a Lecturer in the School of Computing and Information Science.
Data Mining and Machine Learning,
Data Mining and Machine Learning
Programming (Python, C++, Java)
Automata Theory Languages, and Computation
Maktabar Oghaz, M., Zainal, A., Maarof, M.A. and Kassim, M.N. (2018). Content Based Fraudulent Website Detection Using Supervised Machine Learning Techniques. Hybrid Intelligent Systems, [online] 11, pp.294–304.
Maktabdar Oghaz, M., Argyriou, V., Monekosso, D. and Remagnino, P. (2019a). Skin Identification Using Deep Convolutional Neural Network. In: International Symposium on Visual Computing. Springer.
Maktabdar Oghaz, M., Maarof, M.A., Rohani, M.F., Zainal, A. and Shaid, S.Z.M. (2017). An optimized skin texture model using gray-level co-occurrence matrix. Neural Computing and Applications, [online] 31(6), pp.1835–1853.
Maktabdar Oghaz, M., Maarof, M.A., Zainal, A., Rohani, M.F. and Yaghoubyan, S.H. (2015). A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique. PLOS ONE, [online] 10(8), p.e0134828.
Maktabdar Oghaz, M., Razaak, M., Kerdegari, H., Argyriou, V. and Remagnino, P. (2019b). Scene and Environment Monitoring Using Aerial Imagery and Deep Learning. In: Distributed Computing in Sensor Systems (DCOSS).
Kim, C.E., Maktabdar Oghaz, M., Argyriou, V. and Remagnino, P. (2018). A Comparison of Embedded Deep Learning Methods for Person Detection. In: VISAPP 2019. [online]
Oghaz, M.M., Maarof, M.A., Rohani, M.F., Zainal, A. and Shaid, S.Z.M. (2017). A Hybrid Color Space for Skin Recognition for Real-Time Applications. Journal of Computational and Theoretical Nanoscience, [online] 14(4), pp.1852–1861
Yaghoubyan, S.H., Maarof, M.A., Zainal, A., Kiani, M.J., Rad, F. and Maktabdar Oghaz, M. (2016). A Robust Keypoint Descriptor Based on Tomographic Image Reconstruction Using Heuristic Genetic Algorithm and Principal Component Analysis Techniques. Journal of Computational and Theoretical Nanoscience, [online] 13(8), pp.5554–5568.