Summary
Mostafijur Rahman is a Ph.D. candidate and graduate research assistant at The University of Texas at Austin who builds efficient, robust AI systems for medical imaging and computer vision with a practical, deployment-first mindset. Over nine years he has bridged academia and industry—publishing and producing highlighted architectures (e.g., EfficientMedNeXt, EffiDec3D, LoMiX, EMCAD, CASCADE) while interning or researching at GE HealthCare, NIH, and Bosch BCAI. His work emphasizes computational and data efficiency, robustness, and generalization across 2D/3D segmentation, synthesis, translation, and denoising, often via compact architectures, generative methods, and multimodal learning. Before his Ph.D. he combined applied engineering and teaching as a senior software engineer at Samsung Research and as faculty in Bangladesh, giving him rare end-to-end experience from realtime DSP implementation to leading research projects. Currently on the academic and research job market, he focuses on roles where practical constraints and real-world medical impact drive model and system design. An often-overlooked strength is his track record of shipping both production-oriented code and high-impact research that advances deployable medical imaging solutions.
9 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at The University of Texas at Austin
Master of Science - MS, Computer Software Engineering, Master of Science - MS, Computer Software Engineering at University of Dhaka
Bangla, English