Abstract: Our news are saturated with claims of "facts" made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim "cherry-picking"? In this talk, I will describe a framework that we developed recently for checking facts based on queries over structured data. This framework lets us formulate practical fact-checking tasks---such as reverse-engineering (often intentionally) vague claims, and countering questionable claims---as computational problems. I will also describe some algorithmic and system-building challenges that arise in this framework.
Bio: Jun Yang has been teaching Computer Science at Duke University since receiving his Ph.D. from Stanford in 2001. He is broadly interested in databases and data-intensive systems. He is a recipient of the NSF CAREER Award, IBM Faculty Award, HP Labs Innovation Research Award, and Google Faculty Research Award. He also received the David and Janet Vaughan Brooks Teaching Award at Duke. One of his current passions is computational journalism, the idea of leveraging computation to help preserve and advance journalism, especially in the public interest.