I am a Principal Researcher at Microsoft Research AI in the information and data sciences group.

My current research focuses on causal analysis of large-scale social media timelines, with the vision of making causal question-answering as fast and as common as web search. With hundreds of millions of people publicly reporting on their daily experiences, we can data mine these social media streams to better understand the common and critical situations people are in, the actions they take, and their implications.  These inferences are useful for many applications including decision support tools for individuals and analytics to support policy-makers and scientists.

More broadly, I am interested in using social data to help people find what they want and need; and to this end my work drives towards the goal of extracting from social media useful models of how people behave in the world — people's actions in the world, people's interactions with each other, and the consequences of people's decisions. There are three questions I'm addressing with my research:

Foundations and infrastructure for better social media analysis: I build tools and frameworks to make it easier and faster for people to deeply analyze and explore social media. This includes developing best practices for analysis and making them easy to follow.
Connecting social media to the real-world: To interpret social media, I work on entity linking and study the systematic biases inherent in social media's reflection of the world.
Social systems engineering: I study how the affordances and incentives provided by social systems affect the kinds of information we find in social media.

My previous research interests include JavaScript application monitoring and optimization, as well as improving the reliability of Internet services architectures and operations. I received my Ph.D. and my M.S. from Stanford University, and my B.S. in Electrical Engineering and Computer Science from U.C. Berkeley.