I am a rising fourth-year Computer Science Ph.D. student at Carnegie Mellon University, advised by Eric Xing. I am interested in statistical machine learning and the theoretical challenges that arise from the constraints of real-world data. I have been particularly motivated by healthcare applications, as the challenges of building interpretable, robust systems for inference on high-dimensional data are of critical importance in this area.

I enjoy discussing research. Feel free to contact me on Twitter, by email, or in person (GHC 9005).


My heart is motivated by the promise of precision medicine and my mind is captivated by the puzzles of statistical machine learning. You can find a list of my publications according to Google Scholar, Semantic Scholar, DBLP, or curated on my page here.


GenAMap is an open source platform for visual machine learning of structured association mappings between genotypes and phenotypes.

Functional Retrofitting is a scalable method to combine distributional and relational data.

I do my best to make my research code avilable on my github page.


DeepDoggo: Learning the Answer to “Who's a Good Dog?", as featured on Inverse.com.

I use a countdown page to track ML+CompBio conference submission deadlines.