About

I am a young investigator in natural language processing (NLP) at the Allen Institute for AI. I work on the AllenNLP team, where we are building an open-source platform for performing and packaging research in deep learning and NLP.

I completed my masters in NLP at the University of Washington. My thesis was on Polyglot Text Classification with Neural Document Models – performing text classification in many languages using crosslingual parameter sharing and unlabeled data. I also helped unravel fundamental biases in NLP datasets that inflate our success on difficult AI tasks.

Before graduate school, I was a data scientist and software engineer at several enterprises in Seattle and Boston. I worked on scalable machine learning products for venture capital, security, and churn prediction. I particularly enjoy the unique intersection of software engineering and machine learning.

Before that, I did research in computational neuroscience. I worked on data-intensive research in mapping the neocortex and building brain-machine interfaces.

If you’d like to get in touch, reach out on Twitter or Github.