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.
- Probabilistic graphical models
- Event sequences / time series (patient histories in electronic health records can be modeled as irregular, sparse, and noisy sequences of events)
- Multi-Relational rule learning (inductive logic programming)
- Open source software
- Software design and development
- Programming languages
- Go (the game, but the language is cool, too)
- Effects of common drugs in electronic health records on the survival of patients
- Learning the structure of Markov and Bayesian networks via convex optimization
- Faster optimization for log-linear models
Temporal Inverse Probability Weighting for Discovering Adverse Drug Events Especially in Generic Drugs
Aubrey Barnard, David Page, Peggy Peissig, Meng Hu
Causal Discovery of Adverse Drug Events in Observational Data
PhD Dissertation, Computer Sciences, University of Wisconsin–Madison, 2019
Causal Structure Learning via Temporal Markov Networks
Aubrey Barnard, David Page
Probabilistic Graphical Models 9, 2018
Identifying Adverse Drug Events by Relational Learning
David Page, Vítor Santos Costa, Sriraam Natarajan, Aubrey Barnard, Peggy Peissig, Michael Caldwell
AAAI 26, 2012