People, Data and Analysis
Abstract: Big data is a hot topic in computing. Most research has focused on automatic methods of data processing such as machine learning and natural language processing. Another important direction of research is how to build systems that can store and process massive data sets. An example here is map-reduce.
Unfortunately, what has been lost in the discussion is how people should use data to perform analysis and make decisions. It is unlikely that people will be replaced completely by automated decision-making systems in the near future. Hence, an important question to ask is what should people do and what should computers do? In this talk, I will discuss promising approaches for building interactive tools that allow people to perform data analysis more easily and effectively.
Biography: Pat Hanrahan is the CANON Professor of Computer Science and Electrical Engineering at Stanford University, where he teaches computer graphics. His current research involves visualization, image synthesis, virtual worlds, and graphics systems and architectures. Pat has also worked at Pixar where he developed developed volume-rendering software and was the chief architect of the RenderMan(TM) Interface, a protocol that allows modelling programs to describe scenes to high-quality rendering programs. In addition to Pixar, he has founded two companies — Tableau and PeakStream — and has served on the technical advisory boards of NVIDIA, Exluna, Neoptica, VSee, Procedural and SkyTree.
Professor Hanrahan has received three university teaching awards. He has received two Academy Awards for Science and Technology, the Spirit of America Creativity Award, the SIGGRAPH Computer Graphics Achievement Award, the SIGGRAPH Stephen A. Coons Award, and the IEEE Visualization Career Award. He was recently elected to the National Academy of Engineering and the American Academy of Arts and Sciences.