Dr Ammar Belatreche

Associate Professor

Department: Computer and Information Sciences

Dr. Ammar Belatreche received the Ph.D. degree in Computer Science from Ulster University, UK. He joined Northumbria University in May 2016. He is currently a Senior Lecturer in Computer Science and Programme Leader for the MSc Advanced Computer Science in the Department of Computer and Information Sciences. He is a member of the Computational Intelligence and Visual Computing (CIVC) research group. Previously he worked as a Research Associate in the Intelligent Systems Research Centre (ISRC), Ulster University, then as a Lecturer in Computer Science in the School of Computing and Intelligent Systems, Ulster University, Derry, UK.

He has extensive experience across academic and R&D in the areas of bio-inspired intelligent systems, machine learning, face detection and recognition, structured and unstructured data analytics, capital markets engineering, image processing and understanding. He has led a number of research and consultancy projects and has successfully supervised/co-supervised 8 PhD students to completion. He is a fellow of the Higher Education Academy, an Associate Editor of Neurocomputing and has served as a Program Committee Member and a reviewer for several international conferences and journals.

Campus Address

CIS 305
Northumbria University
Newcastle upon Tyne
NE1 8ST

Email Ammar

Key Publications

Please visit the Pure Research Information Portal for further information
Binary Event-Driven Spiking Transformer, Cao, H., Zhou, Z., Wei, W., Liang, Y., Belatreche, A., Zhang, D., Zhang, M., Yang, Y., Li, H. 29 Apr 2025, Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artifical Intelligence (IJCAI)
BSO: Binary Spiking Online Optimization, Liang, Y., Yang, Y., Wei, W., Belatreche, A., Wang, S., Zhang, M., Yang, Y. 1 May 2025, Proceedings of Machine Learning Research, ML Research Press
QP-SNN: Quantized and Pruned Spiking Neural Networks, Wei, W., Zhou, Z., Belatreche, A., Shan, Y., Liang, Y., Cao, H., Zhang, J., Zhang, M., Yang, Y. 22 Jan 2025, The 13th International Conference on Learning Representations, Appleton, US, International Conference on Learning Representations (ICLR)
QP-SNNS: Quantized and Pruned Spiking Neural Networks, Wei, W., Zhang, M., Zhou, Z., Belatreche, A., Shan, Y., Liang, Y., Cao, H., Zhang, J., Yang, Y. Jun 2025, 13th International Conference on Learning Representations, ICLR 2025, International Conference on Learning Representations (ICLR)
Spiking Vision Transformer with Saccadic Attention, Wang, S., Zhang, D., Belatreche, A., Xiao, Y., Liang, Y., Shan, Y., Sun, Q., Zhang, E., Zhang, M. 22 Jan 2025, The 13th International Conference on Learning Representations, Appleton, US, International Conference on Learning Representations (ICLR)
Ternary Spike-based Neuromorphic Signal Processing System, Wang, S., Zhang, D., Belatreche, A., Xiao, Y., Qing, H., Wei, W., Zhang, M., Yang, Y. 1 Jul 2025, In: Neural Networks
Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models, Zhang, M., Luo, X., Wu, J., Belatreche, A., Cai, S., Yang, Y., Li, H. 4 Jun 2025, In: IEEE Transactions on Neural Networks and Learning Systems
Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism, Lang, Y., Wei, W., Belatreche, A., Cao, H., Zhou, Z., Wang, S., Zhang, M., Yang, Y. 11 Apr 2025, Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence, Washington DC, USA, Association for the Advancement of Artificial Intelligence (AAAI)
A Framework for Analyzing Motion Vector Entropy in H.265 Encoded Videos, Bouzegza, M., Belatreche, A. 13 Oct 2024, Proceedings of the 10th International Conference on Advanced Intelligent Systems and Informatics, Cham, Switzerland, Springer
Efficient Quantum Image Classification Using Single Qubit Encoding, Easom - McCaldin, P., Bouridane, A., Belatreche, A., Jiang, R., Almaadeed, S. 1 Feb 2024, In: IEEE Transactions on Neural Networks and Learning Systems