Title: Making Systems That use Statistical Reasoning Easier to Build and Maintain over Time
The question driving my work is, how should one deploy statistical data-analysis tools to enhance data-driven systems? Even partial answers to this question may have a large impact on cience, government, and industry—each of whom are increasingly turning to statistical techniques to get value from their data.
To understand this question, my group has built or contributed to a diverse set of data-processing systems: a system, called GeoDeepDive, that reads and helps answer questions about the geology literature; a muon filter that is used in the IceCube neutrino telescope to process over 250 million events each day in the hunt for the origins of the universe; and enterprise applications with Oracle and Pivotal. This talk will give an overview of the lessons that we learned in these systems, will argue that data systems research may play a larger role in the next generation of these systems, and will speculate on the future challenges that such systems may face.
Christopher (Chris) Re is an assistant professor in the Department of Computer Science at Stanford University. The goal of his work is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his PhD from the University of Washington in Seattle under the supervision of Dan Suciu. For his PhD work in probabilistic data management, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. Chris’s papers have received four best-paper or best-of-conference citations, including best paper in PODS 2012, best-of-conference in PODS 2010 twice, and one best-of-conference in ICDE 2009). Chris received an NSF CAREER Award in 2011 and an Alfred P. Sloan fellowship in 2013.