Speakers

Dr Shaheer U. Saeed is a Lecturer in Computational Biomedical Engineering at Queen Mary University of London and University College London. His research focuses on developing data efficient and clinically applicable artificial intelligence methods for healthcare, with particular interests in cancer diagnosis, medical imaging, and image-guided interventions. 

A central theme of his work is understanding how AI systems can learn effectively from limited and imperfect clinical data while remaining reliable across patient populations and healthcare environments. He has contributed to advances in medical image segmentation, quality assessment, adaptive learning, and clinical decision support, with applications in prostate, liver, pancreatic, and gynaecological diseases. His research also investigates the quality and consistency of clinical annotations and how algorithm evaluation strategies can be designed to better capture clinically meaningful performance. More broadly, he is interested in developing robust algorithms that require fewer expert annotations, generalise across institutions, and integrate imaging, clinical, and biological information to improve clinical decision-making. Dr Saeed has authored more than 50 peer-reviewed publications and has delivered invited talks at international conferences and institutions including MICCAI, IPMI, MIDL, Stanford University, and the University of Oxford. His work has received national and international recognition, including best paper awards and awards recognising innovation in research and public engagement. 

His research is currently supported through a Cancer Research UK ACED Pathway Award investigating novel artificial intelligence approaches for early cancer detection. Through collaborations with clinicians, researchers, and industry partners, he aims to develop technologies that can support diagnosis, treatment planning, and personalised care, while facilitating their safe, effective, and equitable translation into routine clinical practice.