Matthew Engelhard

Assistant Professor Of Biostatistics & Bioinformatics

Durham, North Carolina, United States
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Summary

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Senior
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Matthew Engelhard is an Assistant Professor of Biostatistics & Bioinformatics at Duke University School of Medicine who builds trustworthy AI for large, longitudinal clinical datasets, with a focus on electronic health records and digital health. He leads NIMH-funded projects developing AI clinical decision support for early autism, ADHD, and mental health distress, translating methods into clinical practice while rigorously evaluating performance and fairness. His work uniquely combines causal reasoning, uncertainty quantification, and incorporation of patient preferences to support patient-centered decision-making. He directs the Duke AI Health Data Science Fellowship and teaches Intro to AI for healthcare professionals, mentoring graduate and medical students across disciplines. With a PhD in Systems and Information Engineering and prior applied research experience at the NSA and in engineering roles, he bridges deep quantitative expertise and practical clinical translation. Based in Durham, NC, he focuses on machine learning for health and psychiatry, emphasizing deployable, interpretable solutions rather than purely theoretical models.
code8 years of coding experience
job12 years of employment as a software developer
bookBachelor’s Degree, Biomedical and Electrical Engineering, Mathematics, Bachelor’s Degree, Biomedical and Electrical Engineering, Mathematics at Duke University
bookDoctor of Philosophy (Ph.D.), Systems and Information Engineering, Doctor of Philosophy (Ph.D.), Systems and Information Engineering at University of Virginia
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Github Skills (6)

phonetics5
acl4
natural-language-processing4
computational-linguistics3
machine-learning2
deep-learning2

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

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mengelhard/mmci_applied_ds

Apr 2021 - Jul 2022

Contributions:264 commits, 420 pushes, 1 branch in 1 year 3 months
Model development exercises and methods demonstrations designed to facilitate collaborative machine learning research
Contributions:1 PR, 14 pushes, 1 branch in 2 years 11 months
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