I was a PhD student in the Department of Computer Science at Columbia University, affiliated with the Natural Language Processing Group. My advisor was Kathy McKeown. I defended my thesis, Data-Driven Solutions to Bottlenecks in Natural Language Generation, in September, 2016.
My PhD research focused on hybrid concept-to-text and text-to-text generation, paraphrase mining and discourse. In particular, I was working on generating natural language justifications for the decisions of machine learning systems.
I received my M.S. in Computer Science from Columbia in 2011. As an M.S. student I worked on argumentation and on detecting influencers in online conversations, advised by Owen Rambow at the Center for Computational Learning Systems.
Prior to grad school, I spent several years working as a software developer at a quantitative hedge fund. The experience had left me with an interest in applications of NLP and machine learning to financial prediction and with a deep appreciation for well-designed, transparent and test-driven enterprise-level software engineering.