Math is proof. Given enough data—and today, we have plenty—we can know. “The right information in the right place just changes your life,” said Stewart Brand. But your life won’t change by itself. Bruce Mau defines design as “the human capacity to plan and produce desired outcomes.” Math informs; design compels. Which matters more? A well-designed collection of flawed information—or an opaque, hard-to-parse, but unerringly accurate model? From mobile handsets to social policy, we need both good math and good design. Which is more critical?
The Great Debate series returns to Strata. In this Oxford-style debate, two opposing teams take opposing positions. We poll the audience, and the teams try to sway opinions. It’ll be a fast-paced, sometimes irreverent look at some of the core challenges of putting data to work.
Dr. Gray obtained degrees in Applied Mathematics and Computer Science from Berkeley and a PhD in Computer Science from Carnegie Mellon, and is an Associate Professor at Georgia Tech and CTO of Skytree, Inc. His research focuses on scaling up all of the major practical methods of machine learning (ML) to massive datasets. He began working on this problem at NASA in 1993 (long before the current fashionable talk of “big data”). His large-scale algorithms helped enable the Top Scientific Breakthrough of 2003, and have won a number of research awards. He served on the National Academy of Sciences Committee on the Analysis of Massive Data and frequently gives invited tutorial lectures on massive-scale ML at top research conferences and agencies.
Monica is a data scientist with a passion for turning data into products, actionable insights, and meaningful stories. As the VP of Data for Jawbone, she focuses on developing data-driven products that promote a healthier lifestyle and on finding stories in the UP wristband data.
Prior to Jawbone, Monica was one of the early members of the LinkedIn data science team, where she developed and improved some of LinkedIn’s key data products for matching jobs to passive candidates, discovering people you may know, and recommending groups you may like.
Monica’s compelling data stories are often picked up by the mainstream press, including the Wall Street Journal, The Economist, NPR and CNN. Monica holds a Ph.D. in Computer Science from CMU, where she focused on text mining and applied machine learning. She authored eight US patents and numerous papers that appeared in top-tier peer-reviewed journals and conference proceedings.
Julie thinks in metaphors and finds beauty in the clear communication of ideas. She is particularly drawn to visual media as a way to understand and transmit information, and is co-author of Beautiful Visualization (O’Reilly 2010) and Designing Data Visualizations (O’Reilly 2012).
Doug VanderMolen is Chief UX Architect of ClearStory Data. Before joining ClearStory Data, Doug led the user experience for Google Analytics, Google AdWords and other Google Ads products. Doug’s designs have helped millions of people intuitively understand and utilize data to make key decisions. Prior to Google, Doug was a key member of the team at MeasureMap, which was acquired by Google in 2006. He received his Masters of Design from the Institute of Design.
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