I am a PhD student at Stanford University advised by Percy Liang and Tengyu Ma. My main research interest is in machine learning with domain shifts – can we build machine learning models that perform well even when the train and test distribution are different, or at least signal low confidence when they are not able to make accurate predictions.
I completed my undergraduate degree in computer science at Carnegie Mellon University with a minor in mathematics, and then spent a fun year working at DeepMind. I’m lucky to have been advised by amazing professors at CMU: Avrim Blum (computational geometry, streaming algorithms), Guy Blelloch (parallel data structures), and Bob Harper (programming languages). I was very lucky to work with many wonderful researchers at DeepMind, including Csaba Szepesvari, Jonathan Uesato, and Pushmeet Kohli on robust evaluation of RL policies, and Ali Eslami, Danilo Rezende, and Murray Shanahan on generative models for videos and 3D scenes.
At CMU, I was involved in parliamentary debate, contest programming, contest math, and TA’ing our introductory undergraduate theory class (15-251). See my resume for more details on my professional experience. I also use Github and Linkedin.