I am a research statistician at Foundation Medicine studying clinical decision support and real world genomic data (Boston, MA). Before joining Foundation Medicine, I worked on analyzing wearable device data from early clinical trials, at Takeda. My research interests also include treatment effect heterogeneity, scalable computing, high-dimensional data, and interpretable machine learning.

In 2019, I completed a postdoctoral fellowship with Francesca Dominici (Harvard Biostatistics) and Cynthia Rudin (Duke Computer Science), developing methods for machine learning interpretability. I received my PhD in 2016 from the Johns Hopkins Bloomberg School of Public Health Biostatistics department under the advisement of Vadim Zipunnikov and Brian Caffo. My main dissertation work was on fast computations for bootstrapping principal component analysis, with applications in brain MRI data. You can find a software implementation of this project in the bootSVD R package alt text.

I try to apply a similar curiosity to my hobbies, reading and experimenting to better understand the sciences of running, swimming, and cooking. And yes, I also make spaghetti plots.