Dr. Mohammad Yaqub

Professor M. Yaqub is a distinguished researcher and educator in biomedical engineering and medical imaging, currently serving on the faculty at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Prior to joining MBZUAI, he spent six years as a postdoctoral fellow at the Institute of Biomedical Engineering, University of Oxford, where he tackled complex challenges in medical image analysis.

With over seven years of industry experience, Professor Yaqub held leadership roles including Vice President of Engineering at Intelligent Ultrasound Limited in Oxfordshire, UK. His career also includes academic appointments as a lecturer at Oxford EMI Training and the IT Learning Centre, University of Oxford, and he continues to serve as a visiting fellow in the Nuffield Department of Clinical Neurosciences and the Oxford Acute Vascular Imaging Centre.

Academic & Research Highlights

• Ph.D. in Biomedical Engineering, University of Oxford, UK
• Honorary Fellow, Nuffield Department of Clinical Neurosciences, University of Oxford (2018)
• Patent Filed (2022): Deep Learning Apparatus and Method for Segmentation and Survival Prediction for Head and Neck Tumors (USPTO Application No. 17849943)
• Best Paper Award, FAIR-MICCAI 2021
• Winner, MICCAI 2021 Research Competition
• Grand Prize Winner, Ericsson Together Apart Hackathon UAE (2021), in collaboration with SEHA and Khalifa University

Publications & Editorial Work

Professor Yaqub has co-authored more than 130 papers in leading journals and conferences such as IEEE Transactions on Medical Imaging, Medical Image Analysis, MICCAI, and Ultrasound in Medicine and Biology. He also co-edited two volumes of Medical Imaging Understanding and Analysis (2020, 2021).

Selected Publications:

• Almakky, I., & Yaqub, M. (2025). Weakly-supervised explainable infection severity classification from chest CT scans. PLoS One, 20(10), e0334431.
• Hassan, S., Salem, M., Papineni, V. R. K., Elsayed, A., & Yaqub, M. (2025, September). MAGNET-AD: Multitask Spatiotemporal GNN for Interpretable Prediction of PACC and Conversion Time in Preclinical Alzheimer. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 348-357). Cham: Springer Nature Switzerland.
• Salem, M., Hassan, S., Papineni, V. R. K., Elsayed, A., & Yaqub, M. (2025, September). DEFUSE-MS: Deformation Field-Guided Spatiotemporal Graph-Based Framework for Multiple Sclerosis New Lesion Detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 257-267). Cham: Springer Nature Switzerland.
• Hassan, S., Akaila, D., Arjemandi, M., Papineni, V., & Yaqub, M. (2025). MINDSETS: Multi-omics integration with neuroimaging for dementia subtyping and effective temporal study. Scientific Reports, 15(1), 15835.
• Syed, N., Saeed, M. E. S., Hussain, S., Mirza, I., Abdalla, A. M., Al Zaabi, E. A., ... & Yaqub, M. (2025). Novel hierarchical deep learning models predict type of leukemia from whole slide microscopic images of peripheral blood. Journal of Medical Artificial Intelligence, 8.
• Lekadir, K., et al. "FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. arXiv." arXiv preprint arXiv:2309.12325 (2023).