Prabhat KC is a Senior Staff Fellow at the US FDA with nine years of experience at the intersection of imaging physics, inverse problems, and machine learning for medical imaging. He leads projects evaluating stability and hallucination risks in AI-based reconstruction algorithms, and develops both physics-based and AI-driven methods for reconstruction, denoising, super-resolution and compressed sensing across CT, MRI, PET and ultrasound. Prabhat combines rigorous task-based and bench evaluation (NPS, MTF, numerical observers) with regulatory review experience—assessing 510(k), IDE and related submissions—to bridge research, validation, and clinical device deployment. He has collaborated with national labs and academia to create synthetic data, virtual phantoms, and objective performance tests, and organized community challenges such as the AAPM 2023 Deep Generative Modeling competition. With a PhD from Carnegie Mellon and a background in mathematics, he brings a rare mix of theoretical rigor, practical code development (PyTorch/Python/C/Octave), and regulatory insight that de-risks adoption of emerging AI in medical imaging.
9 years of coding experience
4 years of employment as a software developer
MASS (Mathematical Advanced Study Semester), Mathematics, MASS (Mathematical Advanced Study Semester), Mathematics at Penn State University
Bachelor’s Degree, Mathematics, Bachelor’s Degree, Mathematics at Mercyhurst University
Doctor of Philosophy (Ph.D.), Materials Science, Doctor of Philosophy (Ph.D.), Materials Science at Carnegie Mellon University
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