Dr. Muhammad Yaqub is a Postdoctoral Researcher, with a specialization in artificial intelligence for medical imaging and healthcare applications. He earned his PhD in Electronic Science and Technology, where his research focused on advanced deep learning methodologies for medical image reconstruction and analysis. His work primarily encompasses MRI reconstruction, medical image segmentation, and the development of robust, scalable, and privacy-preserving AI frameworks for clinical applications. Dr. Yaqub has made notable contributions to the field through numerous publications in high-impact journals and leading international conferences. His research integrates state-of-the-art approaches, including federated learning, self-supervised learning, and graph-based neural networks, to address critical challenges such as data heterogeneity, limited annotated data, and privacy constraints in medical imaging. Beyond his research contributions, Dr. Yaqub actively engages with the academic community as a guest editor, editorial board member, conference session chair, and reviewer for prestigious international journals. He is also involved in collaborative scientific initiatives that advance interdisciplinary research. His work is dedicated to bridging the gap between cutting-edge AI methodologies and real-world healthcare applications, fostering the development of intelligent, reliable, and privacy-aware medical systems.