I am a senior principal researcher in the Natural Language Processing group at Microsoft Research. I am interested in how machines can understand natural language. As such, I work on semantic parsing and question answering (currently Nl2SQL). Previously, I worked on machine reading comprehension, creating one of the first datasets in the field (Machine Comprehension Test (MCTest)) a freely-available set of 660 fictional stories and reading comprehension questions, intended to act as a benchmark for measuring how well a machine understands a short passage of text.
I am broadly interested in human language, how we acquire it, how we assign meaning to it, and how we can enable machines to understand natural language as well as we do.
My past interests have included Web mining, social networking, query log analysis, web ranking, mass collaboration, online advertising, and online communities. I tend to work within the fields of machine learning and data mining, applied to various areas of interest.
I graduated with a Ph.D in Computer Science from the University of Washington in 2004. My thesis was on learning and inference in collective knowledge bases, a topic I am still quite interested in. I received my Bachelor’s degree in Computer Science from the California Institute of Technology (better known as Caltech) in 1997.