Visual Data Analysis: Why? When? How? with Pat Hanrahan and Tamara Munzner

In many situations, complex decisions need to be made by people working with computers. Why does visual analysis help with those decisions? When should people be involved and when can automated approaches make these decisions? How can we build effective visual interfaces for data? This discussion will tackle these issues and related questions.

Pat Hanrahan

Pat Hanrahan is the CANON Professor of Computer Science and Electrical Engineering at Stanford University. His current research involves graphics systems and architectures, hardware design tools, programming languages, image synthesis, and visualization. Early in his career, Pat worked at Pixar where he developed software for volume rendering and was the chief architect of the RenderMan(TM) Interface - a protocol that allows modeling programs to describe scenes to high quality rendering programs. He founded PeakStream, BeBop, and Tableau, and has served on multiple technical advisory boards. Pat has received three 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 is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. In 2019, he shared the ACM A.M. Turing Award with Ed Catmull.

Tamara Munzner

Tamara Munzner is a Professor of Computer Science at the University of British Columbia and holds a PhD from Stanford. She has been active in visualization research since 1991, has published over eighty papers, and co-chaired InfoVis and EuroVis. Her book Visualization Analysis and Design appeared in 2014, and she received the IEEE VGTC Visualization Technical Achievement Award in 2015. She is co-editor of the AK Peters Visualization book series from CRC/Routledge. She has worked on problem-driven visualization in many domains ranging from genomic to e-commerce to journalism. Her technique-driven visualization interests include graph drawing and dimensionality reduction. Her evaluation interests include controlled experiments in a laboratory setting and qualitative studies in the field.