Sharib Ali

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies.

• PhD in Medical Image Analysis, University of Lorraine, France
• MSc in Computer Vision (by research), University of Burgundy, France

Dr. Ali's research focuses on biomedical image analysis, computer vision, and machine learning, with applications in early cancer detection, computational endoscopy, and 3D reconstruction. He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings.

The recent publications highlight a strong trend in generalisability assessment, multi-modal data fusion, and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning, mixed reality in surgery, and multi-centre datasets, addressing key challenges like bias, data imbalance, and privacy.

Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare.

He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis.

His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.