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.
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. Dafna'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.