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Mahdi Mobarak-Abadi

Brain

Mahdi's journey began with a background in Bioelectric Engineering, where he focused on developing innovative solutions for medical imaging challenges. During his Master’s studies, he designed a deep learning-based motion correction algorithm for spinal cord functional MRI, improving the accuracy of imaging analyses in this critical area. 


Currently, his research explores the intersection of advanced neuroimaging and machine learning. He leverages graph theory and network metrics to analyze functional brain networks, with a particular focus on understanding and improving outcomes in epilepsy treatment. His work involves developing automated methods to identify and classify noisy signals in fMRI data, contributing to more reliable analyses and insights into subcortical-cortical connectivity. By combining expertise in bioelectric engineering, machine learning, and neuroimaging, Mahdi aims to bridge the gap between advanced research and clinical applications, driving innovation in healthcare.

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