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.
Alexander Gray is an associate professor at Georgia Tech and the CEO of Skytree, Inc. His research focuses on scaling up all of the major practical methods of machine learning (ML) to massive datasets. Alex 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.
Alex 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. Alexander has degrees in applied mathematics and computer science from UC Berkeley and a PhD in computer science from Carnegie Mellon.
Monica Rogati is an independent data science executive and advisor who has built key data products and teams at Jawbone and LinkedIn; she now helps startups make the most out of their data. As the VP of data at Jawbone, Monica built Jawbone’s data science and engineering team, focusing on developing data products that helped millions lead healthier lives. Her team also analyzed Jawbone’s wearable data to derive novel insights about sleep, movement, and food, then turned these insights into smart product features, compelling data stories, and interactive visualizations. At LinkedIn, Monica was one of the early members of the data science team. She developed LinkedIn’s key data products for job matching and recommendations, and she doubled the effectiveness of the “people you may know” machine-learning algorithm that drives the growth of LinkedIn’s connection graph. Monica is also an equity partner at Data Collective, an early-stage VC firm focused on the big data space.
Monica’s data stories have been published in the Wall Street Journal, the New York Times, and Time and on NPR and CNN. Fast Company recognized her as one of the 100 most creative people in business, and Fortune named her as one of the Big Data All-Stars. She has published numerous academic papers in top-tier journals and conferences and is frequently invited to keynote industry and academic conferences. She has a PhD in computer science from Carnegie Mellon, where she focused on text mining and machine learning, but is now focused on applied data science.
Julie Steele 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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