Bret Nestor is a research-focused computer scientist with a decade of experience developing robust, governable machine learning models for clinical time-series data. Holding a PhD from the University of Toronto and advanced training at UC Berkeley, he blends biomedical engineering and mechanical engineering roots with rigorous ML research developed during postdoctoral positions at Harvard and UBC and a PhD stint at the Vector Institute. His work centers on ensuring clinical models generalize across patient populations and sites, addressing distributional shifts to prevent misleading diagnoses in deployment. Beyond theory, Bret has practical lab and engineering experience—from microfluidics and biomimetic systems to biofilm fabrication—that gives him a rare, translational perspective on healthcare AI. He publishes actively on model reliability and governance, and brings a track record of bridging experimental biology and machine learning to create safer clinical tools.
10 years of coding experience
7 years of employment as a software developer
Master of Engineering (MEng) Bioengineering and Biomedical Engineering, Master of Engineering (MEng) Bioengineering and Biomedical Engineering at University of California, Berkeley
Bachelor of Applied Science (BASc) Mechanical Engineering, Bachelor of Applied Science (BASc) Mechanical Engineering at The University of British Columbia
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Toronto
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