Aubrey Barnard

Portrait of Aubrey Barnard 4720 Medical Sciences Center
1300 University Ave
Madison, WI 53706

Curriculum Vitæ | Résumé

I do machine learning research, focusing on medical applications of causal discovery in electronic health records databases. My research interests include causal inference, probabilistic graphical models, event history analysis / time series, multi-relational rule learning, and databases.

In 2019, I earned my PhD in Computer Sciences from the University of Wisconsin, advised by David Page (who has moved to Duke). My dissertation was on discovering the adverse effects of medications, through learning the structure of Bayesian network causal models, and through analyzing observational studies with machine learning for hypothesizing drug effects. This research produced a new method for Bayesian network structure learning, and a novel causal discovery machine learning approach based on analyzing before–after studies with temporal inverse probability weighting.

While I mostly work with Python (e.g., scikit-learn, NumPy / SciPy, matplotlib, PyTorch), I have been writing all my numerical code in Julia. It is as easy to use as Python—it is interactive, high-level, expressive, multi-paradigm, dynamically-typed—but it runs at machine speed and has linear algebra and concurrency built in. I encourage you to check Julia out!

I ran the UW–Madison ML and AI Reading Group for 5 semesters.

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