I am a Research Scientist working at the intersection of medical imaging AI, computational neuroscience, neuroimaging, and clinical imaging informatics.

My background in Medical Physiology provides me with a strong foundation in human biology, neurophysiology, and clinical science, while my work has increasingly focused on applying machine learning and computational methods to medical imaging and brain research.

My current interests include 3D medical image segmentation, brain MRI analysis, medical foundation models, imaging quality control, multimodal AI, and the translation of AI models into real clinical imaging workflows.

I have worked on projects involving brain tumour segmentation, BraTS-related datasets, MRI quality assessment, PACS/RIS systems, DICOM workflows, radiology AI, and GPU-enabled research infrastructure. I also co-led a workshop at Deep Learning Indaba 2026 on lightweight brain tumour segmentation using 3D U-Net for resource-constrained systems and served as a poster judge.

Beyond algorithm development, I am interested in the complete medical imaging ecosystem — from data acquisition and quality control to model development, validation, PACS integration, interoperability, and clinical deployment.

My long-term goal is to pursue advanced research in computational neuroscience, neuroimaging, and medical image computing, with particular interest in developing clinically meaningful AI systems that are robust, scalable, and relevant to African healthcare environments.