About
My research is on the intersection of developing new algorithms for machine learning and new applications. In the area of algorithms, I’m particularly interested in (1) making deep networks more modular and richly structured and (2) improving the generalization performance of deep networks, especially across shifting domains. I am particularly interested in techniques which use functional inspiration from the brain and psychology to improve performance on real tasks.
In terms of applications of Machine Learning, I’m interested in pretty much everything. My most recent applied work has been on historical Japanese documents and has resulted in KuroNet (opens in new tab), a publicly released service which makes classical Japanese documents (more) understandable to readers of modern Japanese. At Amazon, I worked on systems for estimating how much products will sell in the future. As an undergraduate, I developed text classifiers for Twitter to help measure and monitor flu outbreaks.