William Boag
Senior Healthcare Data Lead
Cambridge, Massachusetts, United States
Summary
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Senior🎓
Top SchoolWilliam Boag is a Senior Healthcare Data Lead based in Cambridge, MA with 11 years of experience applying machine learning and policy insight to improve patient outcomes across academic, nonprofit, and government settings. He holds a PhD from MIT where his research probed how mistrust and policy interact with ML-driven healthcare decisions and produced work on radiology report generation and fairness-aware pipelines. At Duke Health he built predictive models to flag high-risk pregnancies to prevent sepsis and postpartum hemorrhage, and now leads data efforts for MassHealth serving Medicaid and CHIP populations. His background spans NLP, clinical concept extraction, and deploying end-of-life care models, reflecting a rare blend of rigorous research and production-focused deployment. William’s career choices show a consistent throughline: using technical skill to address structural inequities in healthcare rather than pursuing pure model novelty. Outside work he signals regional roots and personality with references like Terrier, River Hawk, Beaver, hinting at New England ties and a pragmatic, industrious approach.
12 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Massachusetts Institute of TechnologyDiploma, Diploma at Dracut Senior High SchoolBachelor of Science (BS), Computer Science, 4.0, Bachelor of Science (BS), Computer Science, 4.0 at University of Massachusetts at LowellMechanical Engineering, Mechanical Engineering at Boston University
Github Skills (13)
named-entity-recognition10
natural-language-processing9
representations9
derived9
cosine-similarity8
vector5
machine-learning4
keras4
sequence-to-sequence3
entity1
nlp1
health1
language-processing1
Programming languages (3)
Github contributions (5)
text-machine-lab/CliNER
Aug 2014 - Jul 2020
Clinical Named Entity Recognition system (CliNER) is an open-source natural language processing system for named entity recognition in clinical text of electronic health records.
Contributions:1 release, 133 commits, 6 PRs in 6 years
named-entity-recognitionnatural-language-processing
text-machine-lab/TwitterHawk
Aug 2014 - Nov 2015
Contributions:75 commits, 8 pushes in 1 year 3 months
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