I am a research assistant in the Johns Hopkins NeuroData Lab where my work focuses on oblique random forests for data with structure, developing a Python library of multiview learning methods, and running statistical analyses on fMRI data sets. I graduated from Johns Hopkins in 2019 with a Bachelor’s degree in Applied Mathematics & Statistics and in 2020 with a Master’s degree in Biomedical Engineering during which I completed my thesis on oblique random forests. Beyond my immediate work, my interests are broad but include statistical decision theory, methods for network-valued data, and geospatial visualizations. Check out some of my projects and feel free to contact me!
In my free time, I enjoy running, climbing, swing dancing, baking sourdough bread, board games, ultimate frisbee, and reading.