Research Description
Chris is in the Computer Science department at Stanford, and his work integrates statistical processing techniques with data processing systems to make such systems easier to build and deploy. One of his projects is building a system called DeepDive that extracts knowledge buried in dark data which includes text, tables, figures, web pages, and scientific articles. DeepDive-based systems have recently shown the ability to read scientific articles from multiple domains with accuracy that can match—and even exceed—human readers. The hope is that these systems can help people bring more of the world’s data to attack pressing challenges. DeepDive are currently used in drug repurposing, materials science, and building a knowledge base to help diagnose rare diseases. More recently, DeepDive is in use by law enforcement in the fight against human trafficking.
His lab is also interested in many aspects of data processing, and he recently helped discover the first join algorithm with worst-case optimal running time, which uses a novel connection between logic, combinatorics and geometry. In addition, work from his group has been incorporated into scientific efforts including the IceCube neutrino detector, Cloudera's Impala, and products from Oracle, Pivotal, Google Brain and Microsoft's Adam.
Chris is the recipient of a National Science Foundation CAREER Award, an Alfred P. Sloan Research Fellowship, the VLDB Early Career Award, and the Macarthur Foundation Fellowship. Chris received his PhD from the University of Washington in Seattle.
More information:
Google Scholar Profile
Lab Group on GitHub
DeepDive Website
Message sent
Thank you for sharing.
related links
Data-Driven Discovery 
Science
Stanford University, Department of Computer Science
Back