Rate My Professor Amr Ahmed

Professor Amr Ahmed serves as Head of the Department of Computer Science at Edge Hill University. He earned his Bachelor’s degree in Electrical Engineering from Ain Shams University in 1993, an M.Sc. degree by research in Computer and Systems Engineering from the same institution in 1998, a Ph.D. in Computer Graphics and Animation from the University of Surrey in 2005, and a Postgraduate Certificate from the University of Lincoln in 2010. Prior to his current role, Ahmed held positions including Head of School of Computer Science at the University of Nottingham Malaysia Campus, academic staff at the University of Lincoln since 2005, Research Fellow at the University of Surrey, and Research Scientist at Sharp Labs of Europe in Oxford, along with other industry roles in engineering consultancy.

Ahmed’s research specializes in applications of artificial intelligence and machine learning integrated with computer vision and image/video processing for the analysis, understanding, and interpretation of visual and textual contents. His primary interests include supporting medical diagnosis through liver characterisation, lesion detection, segmentation, and classification; personalised health screening and monitoring; content-based image retrieval; and multimodal data fusion. Further areas encompass video annotation, scene understanding, semantic analysis, video matching and similarity, and handwriting recognition, particularly in Arabic. He leads the Medical Image/Video Analysis Research Group and is a member of the Health Research Institute. Ahmed has supervised several PhD and MSc by research students as well as research assistants. Notable recent publications include “HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology” (2026), “Training-Only Heterogeneous Image-Patch-Text Graph Supervision for Advancing Few-Shot Learning Adapters: TOGA” (2026), “Post Hoc Interpretability of Deep Learning Models for Breast Cancer Histopathological Images with Variational Autoencoders” (2025), and “A Neurophysiological Model based on Resting-State EEG Functional Connectivity Features for Assessing Semantic Long-term Memory Performance” (2024). He is a member of the British Computer Society, the International Association of Engineers, and the IEEE Computer Society.