A short bio

As a Master’s student in Data Science at the University of Amsterdam, Amelia Soare brings a background in evaluating healthcare innovation through patent data analytics. She is now particularly engaged in the dynamic field of privacy-preserving machine learning for healthcare. Amelia's research centers on federated learning, a pioneering method that allows hospitals to collaboratively analyze patient records without sharing sensitive data. She is dedicated to building and optimizing federated learning pipelines for clinical data, especially structured records like diagnoses and laboratory results, which are crucial for medical research but are often overshadowed by imaging data in the literature. Working closely with fellow researchers and medical data experts at SEEDBiomed, Amelia is systematically evaluating privacy-focused technologies and model architectures to achieve strong performance without compromising regulatory compliance or patient. Her work seeks to advance secure, practical frameworks for collaborative healthcare research, supporting the next generation of data-driven medical innovation.