Paul Schmiedmayer is an instructor and AI leader at Stanford with nine years of experience building data-driven, patient-facing digital health systems that run on resource-constrained devices. He leads digital health and AI efforts at the Mussallem Center for Biodesign and helps develop the Stanford Spezi ecosystem to enable interoperable, privacy-preserving clinical interventions and novel digital biomarkers. His work spans academic research, applied AI, teaching CS342, and hands-on engineering—shipping models to smartphones and smart devices to create scalable closed-loop care. A Swift enthusiast with a background from Technical University of Munich and research stints at CMU and Apple, he blends rigorous doctoral-level methods with product-minded implementation. Paul’s uncommon focus is on validating AI in real-world deployments that balance cost, privacy, and equity, not just algorithmic performance.
9 years of coding experience
8 years of employment as a software developer
Doctoral Candidate, Computer Science, Doctoral Candidate, Computer Science at Technical University of Munich
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Paul Schmiedmayer - Instructor at Stanford University