Skip to main content

Thorn in the Side of Big Data: Too Few Artists

Chris Re (Stanford University)
Data Science
Ballroom AB
Average rating: ****.
(4.18, 11 ratings)

A new generation of data processing systems, including web search,
Google’s Knowledge Graph, IBM’s Watson, and several different
recommendation systems, combine rich databases with software driven by
machine learning. The spectacular successes of these trained systems
have been among the most notable in all of computing and have
generated excitement in health care, finance, energy, and general
business. But building them can be challenging even for computer
scientists with PhD-level training. If these systems are to have a
truly broad impact, building them must become easier. This talk
describes our recent thoughts on one crucial pain point in the
construction of trained systems feature engineering. For street-art
lovers, this talk will argue that current systems require artists,
like Banksy, but we need them to be usable by the Mr. Brainwashes of
the world.

Chris Re

Assistant Professor, Stanford University

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.