Mads Olsen is a Data Science Manager and computational sleep science expert with a PhD in biomedical engineering and nine years of experience building machine learning systems that characterize sleep and diagnose sleep disorders. He has led algorithm development and interpretable model work bridging clinical and engineering teams at Phastar and consulted with Stanford and Takeda to improve diagnostic accuracy using deep learning and large-scale wearables data. His research-driven approach focuses on deployable, explainable solutions for prehospital and post-hospital monitoring, translating wearable signals into objective sleep profiles. Skilled in optimization, data analysis, and cross-functional collaboration, he combines academic rigor with product-facing delivery in Cambridge’s health-tech ecosystem. Notably, his PhD platform aimed to shift sleep medicine toward in-home, continuously learning diagnostics—an early example of scalable personalized healthcare.
9 years of coding experience
2 years of employment as a software developer
Master's degree, Medical Engineering, Master's degree, Medical Engineering at Danmarks Tekniske Universitet
Bachelor's degree, Fødevarevidenskab, Bachelor's degree, Fødevarevidenskab at Københavns Universitet
Visiting research student, Biomedical engineering, Visiting research student, Biomedical engineering at Stanford University
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