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Large-scale data has the potential to transform almost every aspect of our world, from science to business; To realize this potential, we must convert data into insight.
In this talk I will describe two of my attempts to address this problem
computational:
The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or areas of research. 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 automatically discovering insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends research directions that are both new and promising.
I will formulate both problems mathematically and provide efficient, scalable methods to solve them. User studies on real-world datasets show that our methods help users efficiently gain insight across multiple domains.
Please take the opportunity to connect and share this video with your friends and family if you find it useful.
