Abhijeet Parida is an R&D DevOps Engineer with nine years of experience applying deep learning and systems engineering to medical imaging and pediatric healthcare research. Based at Children's National Hospital and the Pediatric Accelerated Intelligence Lab, he builds and deploys AI pipelines for MRI harmonization, brain tumor segmentation, federated learning, and foundation-model research. With an MS from TUM in Computational Science and Engineering and a mechanical engineering background, he combines rigorous modeling instincts with practical deployment skills honed at deepc, where he shipped open-source tools like brainseg and nekton. His PhD work in electronic systems engineering and continued interest in few-shot/meta-learning fuel data-efficient approaches to clinical problems. Known for making AI accessible through web apps, hackathons, and teaching, he bridges research, production, and community impact in healthcare AI.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Technology (BTech), Mechanical Engineering, Bachelor of Technology (BTech), Mechanical Engineering at Amrita Vishwa Vidyapeetham
Master of Science (MS), Computational Science and Engineering, Master of Science (MS), Computational Science and Engineering at Technical University of Munich
PCMB, PCMB at Navy Children School, Goa
Doctor of Philosophy - PhD, Electronic Systems Engineering, Doctor of Philosophy - PhD, Electronic Systems Engineering at Universidad Politécnica de Madrid
Contributions:27 pushes, 1 branch in 7 years 2 months
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