Summary
Seyed-ahmad Ahmadi is a Senior Solution Architect at NVIDIA specializing in deep learning for healthcare, with five years of industry experience and a strong academic foundation in medical image analysis and multimodal clinical data. He has led translational AI research—from founding a medical AI group at LMU’s German Center for Vertigo and Balance Disorders to securing and coordinating major grants for 3D ultrasound and Parkinson’s diagnostics—and helped develop influential segmentation models (e.g., V-Net, Hough-CNN) now widely cited in the field. At NVIDIA he advises clinical and research teams on geometric deep learning, graph neural networks, and computer-aided diagnostic decision support, bridging cutting-edge research with deployable solutions. Based in Munich, he combines rigorous doctoral training and postdoc leadership with practical product-focused architecture, and uniquely pairs expertise across ultrasound, MRI/CT atlasing, and motion/gait modalities to tackle complex multimodal healthcare problems.
5 years of coding experience
13 years of employment as a software developer
Non-degree extra-curricular program (4 semesters), Entrepreneurship/Entrepreneurial Studies, Non-degree extra-curricular program (4 semesters), Entrepreneurship/Entrepreneurial Studies at Center for Digital Technology and Management (CDTM)
Master’s Degree, Electrical, Electronics and Communications Engineering, 1.6 (best: 1.0, worst: 4.0), Master’s Degree, Electrical, Electronics and Communications Engineering, 1.6 (best: 1.0, worst: 4.0) at Technical University Munich
Master’s Degree, Electrical, Electronics and Communications Engineering, 3.7 (best: 4.0, worst: 1.0), Master’s Degree, Electrical, Electronics and Communications Engineering, 3.7 (best: 4.0, worst: 1.0) at Georgia Institute of Technology
Non-degree Graduate Level Coursewor, Electrical and Computer Engineering, Non-degree Graduate Level Coursewor, Electrical and Computer Engineering at National University of Singapore