I'm a NASA Hubble research fellow based at the University of Michigan. I build end-to-end machine learning systems for large-scale, real-world data (e.g. noisy, messy, unlabeled) at the intersection of image, time-series, and radio waveform datasets. I am particularly interested in leveraging advances in deep learning for building robust and interpretable forward models (see my current work on the BayesLIM project).
See below for some of my open-sourced software contributions, and see here for some highlights of my research.