Summary
Boudewijn Aasman is a data scientist and advisor based in New York with 11 years of experience building and operationalizing machine learning for healthcare. He has led interdisciplinary teams at Montefiore and Albert Einstein to deploy real-time clinical models—ranging from LSTM-based early ARDS/respiratory-failure detection to RL-driven treatment-pathway optimization—and built the MLOps tooling (Dagster, Docker, Kubernetes, Spark) that kept them running in production. His work spans end-to-end pipelines, custom loss functions for clinical relevance, and data governance frameworks used institution-wide, and contributed to multi-hospital ML systems now in routine clinical use. At Cognome he advises on next-generation clinical decision support and LLM ensembles for precision medicine, bringing a rare combination of hands-on model engineering, deployment experience, and published research. An analytical thinker who started in semantic data lakes and graph-integrated analytics, he pairs deep technical craft with the ability to translate results for clinical and leadership audiences.
11 years of coding experience
5 years of employment as a software developer
California Polytechnic State University, San Luis Obispo
English, Dutch