Machine Learning Research Scientist at University of Wisconsin-Madison
Madison, Wisconsin, United States
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Summary
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Senior
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Aubrey Barnard is a machine learning research scientist with 14 years of experience specializing in discovering causal relationships from observational data via probabilistic causal models and efficient algorithms. Based at the University of Wisconsin–Madison, they have led projects ranging from matched case–control studies for early ovarian cancer detection to convex-optimization approaches for Bayesian network structure learning and scalable log-linear Markov network inference. Their work blends theoretical CS rigor with practical impact—building tools to clean and normalize clinical lab data, shepherding IRB studies, and winning grants for causal-structure research. Aubrey’s portfolio emphasizes algorithmic efficiency and filling methodological gaps, from faster independence tests to non-combinatorial structure learning, and they mentor students while translating research into applied health analytics. An unusual strength is combining deep probabilistic modeling with hands-on systems work (e.g., convolution and Gaussian mixture–based cleaning pipelines), making them adept at both foundational methods and real-world deployments.
14 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Wisconsin-Madison
Bachelor of Arts (BA), Computer Science, Music Theory & Composition, Bachelor of Arts (BA), Computer Science, Music Theory & Composition at St. Olaf College
Contributions:8 pushes, 11 branches in 6 years 8 months
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Aubrey Barnard - Machine Learning Research Scientist at University of Wisconsin-Madison