The Aha! Moment: From Data to Insight

Shahaf, Dafna

Title:

The Aha! Moment: From Data to Insight

dc.contributor.author	Shahaf, Dafna	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Computational Science and Engineering	en_US
dc.contributor.corporatename	Stanford University	en_US
dc.date.accessioned	2014-03-04T18:56:51Z
dc.date.available	2014-03-04T18:56:51Z
dc.date.issued	2014-02-07
dc.description	Presented on February 7, 2014 from 11:00 to 12:00 pm in room 1116 West of the Klaus Advanced Computing Building on the Georgia Tech campus.\|Dafna Shahaf is a postdoctoral fellow at Stanford University. She received her Ph.D. from Carnegie Mellon University; prior to that, she earned an M.S. from the University of Illinois at Urbana-Champaign and a B.Sc. from Tel-Aviv University. Shahaf's research focuses on helping people make sense of massive amounts of data. She has won a best research paper award at KDD 2010, a Microsoft Research Fellowship, a Siebel Scholarship, and a Magic Grant for innovative ideas.	en_US
dc.description	Runtime: 52:45 minutes.	en_US
dc.description.abstract	The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally. The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising. I will formulate both problems mathematically and provide efficient, scalable methods for solving them. User studies on real-world datasets demonstrate that our methods help users acquire insight efficiently across multiple domains.	en_US
dc.format.extent	52:45 minutes
dc.identifier.uri	http://hdl.handle.net/1853/51308
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	Computational Science and Engineering Seminar Series	en_US
dc.subject	Data mining	en_US
dc.subject	Automatic discovery of insightful connections in data	en_US
dc.subject	Real-world datasets	en_US
dc.title	The Aha! Moment: From Data to Insight	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Computational Science and Engineering
local.relation.ispartofseries	Computational Science and Engineering Seminar Series
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