Portrait

I am Md Mostafijur Rahman, a final year Ph.D. Candidate at The University of Texas at Austin advised by Prof. Radu Marculescu. You can view my CV here and reach me at mostafijur.rahman [at] utexas.edu.

I design and build AI systems that make medical imaging and computer vision more efficient, trustworthy, and accessible in low-resource settings. My research treats practicality as a first-class concern, focusing on computational efficiency, data efficiency, robustness, and generalization through efficient architectures, foundational models, robust training, generative, and multi-modal learning methods for 2D/3D segmentation, classification, synthesis, translation, and denoising. I've developed methods such as: LoMiX (NeurIPS ’25), a plug-and-play supervision module for stable multi-scale learning; EfficientMedNeXt (MICCAI ’25 Highlight), a 3D medical image segmentation architecture that delivers end-to-end efficiency; EffiDec3D (CVPR ’25 Highlight), an optimized 3D decoder that boosts accuracy at lower cost; EMCAD (CVPR ’24), an efficient multi-scale convolutional-attention decoder that strengthens 2D segmentation; and CASCADE (WACV ’23), an early cascaded attention-based decoder for medical image segmentation.

During my Ph.D. study, I completed research internships at GE HealthCare, the National Institutes of Health (NIH), and Bosch AI. I was named a 2026 MLCommons ML and Systems Rising Star and received the NIH Summer IRTA Fellowship. My work has also been recognized with the Texas Health Catalyst Award and the Discovery to Impact Award.

Before joining UT Austin, I spent over four years as a faculty member at the University of Barishal, Bangladesh. I also worked as a Senior Software Engineer at Samsung Research Bangladesh.

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Address

EER 5.838B
2501 Speedway
Austin, TX
United States
78712

Email

Academic
mostafijur.rahman [at] utexas.edu

Other Use
mostafij.csebu [at] gmail.com

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