Discovery Informatics Symposium: AI Takes a Science-Centered View on Big Data

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Date: 
Friday, November 15, 2013 (All day) to Sunday, November 17, 2013 (All day)
Location: 
Arlington, VA

Discovery Informatics Symposium:
AI Takes a Science-Centered View on Big Data

November 15–17, 2013, Arlington, VA (USA)
AAAI Fall Symposium Series

http://www.discoveryinformaticsinitiative.org/dis2013

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Discovery Informatics focuses on intelligent systems aimed at accelerating discovery, particularly in science but also from any data-rich domain. It is a generalization of scientific informatics work (e.g., medical-, bio-, eco- or geo-informatics) that seeks to apply principles of intelligent computing and information systems in order to understand, automate, improve, and innovate any aspects of discovery processes. A range of AI research is directly relevant including process representation and workflows; intelligent interfaces; causal reasoning; machine learning; knowledge representation and engineering; semantic web; advanced visualization toolkits and social computing.

The application of AI approaches to assist in scientific discovery is an open ended knowledge-driven challenge with a very high potential impact. This is especially true in this era of ‘big data’, which provides the theme of this  symposium.

TOPICS

This symposium will provide a forum for researchers interested in understanding the role of AI techniques in improving or innovating scientific processes. We encourage submissions that: (1) build on success stories that provide a contextual understanding of why certain approaches worked in scientific domains; (2) push the envelope of discoveries in big data; (3) characterizes the act of discovery as a computing challenge for intelligent systems.  

Specific topics of discussion include, but are not limited to:

•    What are the broad AI challenges in discovery in big data?
•    How can we support the way scientists approach big data?
•    How do we get to big data from smaller data through automated or assisted integration and aggregation?
•    What integrated AI capabilities are needed to tackle big data in science?
•    How can we improve our understanding of science and discovery processes and the role of AI in the context of those processes?
•    How can we capture science processes and open them to scientists in other disciplines and the broader public?
•    Can AI be effective in facilitating insights and looking for knowledge gaps using big data?

The symposium will be organized around thematic sessions.  Each session will include paper presentations and in some cases invited speakers, followed by discussions.

Submissions should be up to 6 pages, using the AAAI style files. Submissions shall be made through the EasyChair website : https://www.easychair.org/conferences/?conf=dis2013.

ORGANIZING COMMITTEE
Gully APC Burns, University of Southern California
Yolanda Gil, University of Southern California
Yan Liu, University of Southern California
Natalia Villanueva-Rosales, University of Texas at El Paso

IMPORTANT DATES
Submission deadline: May 24, 2013
Notification to authors: June 31, 2013
Camera-ready due: September 2, 2013
Registration deadline: September 20, 2013
Symposium: November 15-17, 2013