Subah is a Ph.D. candidate at the Weldon School of Biomedical Engineering and an American Association of University Women (AAUW) International Doctoral Degree Fellow. She received her undergraduate degree in Electrical and Electronic Engineering (EEE) from the Bangladesh University of Engineering and Technology (BUET), with a major in Communication and Signal Processing. Her research investigates label-efficient learning strategies for medical data, where annotations are scarce and expensive. Specifically, she explores semi-supervised and few-shot learning frameworks for segmentation and disease characterization, including the use of foundation models such as DINO, MedSAM, and CLIP, to enable knowledge transfer under limited supervision. In parallel, she studies domain adaptation using generative adversarial networks to improve generalization across datasets, and complements deep learning with interpretable, classical machine-learning approaches to support clinical relevance and translational impact. When not engaged in research, she enjoys reading and traveling. LinkedIn: https://www.linkedin.com/in/mohseu-rashid-subah/