Summary
Justin Lee is a research data scientist with eight years of experience applying statistical modeling and machine learning to physiological time-series problems in cardiology, neuroscience, and aerospace. He builds end-to-end data solutions—pipelines, models, and visualizations—using Python, R, SQL, and AWS (including SageMaker and Athena) and has operationalized algorithms in regulated environments like IEC 60601. At Philips he led arrhythmia classification and ambulatory ECG test system modernization; at Metron he adapts ML decision aids to aerospace and defense use cases. His background in neural electrophysiology and biomedical engineering gives him a strong signal-processing and biomechanics perspective that informs feature design and model validation. Comfortable with both research experiments and production constraints, he blends rigorous statistical methods with pragmatic software tooling and automation. Checkable work and reproducible examples are available on his GitHub, reflecting a hands-on approach to applied research.
8 years of coding experience
6 years of employment as a software developer
Master of Engineering (M.Eng.), Biomedical/Medical Engineering, Master of Engineering (M.Eng.), Biomedical/Medical Engineering at Duke University
Bachelor of Science (B.S.), Biomedical/Medical Engineering, Bachelor of Science (B.S.), Biomedical/Medical Engineering at The George Washington University
Westwood High School