I’m a research associate at University of Pennsylvania who studies machine learning and biomedical informatics. Very soon, I’ll be starting a lab as an Assistant Professor in the Computational Health Informatics Program at Boston Children’s Hospital and Harvard Medical School. If you’re looking to do a post-doc or graduate program at the intersection of interpretability, fairness, and health informatics, please reach out!
I am interested in understanding the underlying dynamics of complex, real-world systems in human-interpretable terms. My research tends to focus on methods that automate data science in order to achieve this goal. Basically, I would like my computer to do my job for me. That includes tasks like cleaning data, writing code, and explaining scientific results. I study how to make these systems more fair and interpretable to the people they impact.
I’m currently funded by a Pathway to Independence Award from the NIH. I’m using this award to study methods for interpretable representation learning from electronic health records. I work in the Computational Genetics Lab, which is part of the Institute for Biomedical Informatics. This group is dedicated to identifying the genetic and environmental factors that contribute to human health. Before Penn, I applied most of my research to wind energy, including identification and control of wind turbine dynamics and gearbox reliability.
Short descriptions of different projects are on my research page. This is a list of my papers. I’ve tried to provide preprints for those that are available. Feel free to contact me if you need one that is missing. Here is a bibtex version.