Dr. Wei-Kai Lee received his Ph.D. in Biomedical Imaging and Radiological Sciences from National Yang Ming Chiao Tung University (NYCU), where he specialized in advanced brain MRI analysis and the development of AI-driven medical imaging methodologies. His research focuses on structural and diffusion MRI, deep learning–based lesion segmentation, and multi-center imaging harmonization, with the overarching goal of improving diagnostic accuracy, reproducibility, and clinical workflow robustness. Dr. Lee has published peer-reviewed journal articles and conference papers on topics such as brain tumor annotation, multi-center federated learning frameworks, magnetic resonance fingerprinting (MRF), and next-generation medical image segmentation models. His work integrates computational modeling, quantitative imaging, and machine learning to address key challenges in neuroimaging, including data heterogeneity, domain shift, and the development of generalizable and clinically trustworthy AI systems. Beyond academic research, Dr. Lee has contributed to clinical and industry collaborations involving RESTful API development, medical imaging data pipelines, AI model deployment, and user-interface integration for healthcare applications. He is also actively engaged in teaching and mentoring, creating educational content that incorporates clinical labeling workflows, multimodal MRI interpretation, and practical AI applications across diverse medical imaging modalities.