Workshop on Translational Data Science 2017
Translational data science is a new term that is being used for an emerging field that applies data science principles, techniques, and technologies to challenging scientific problems that hold the promise of having an important impact on human or societal welfare. The term is also used when data science principles, techniques and technologies are applied to problems in different domains in general, including—but not restricted to—science and engineering research. The TDS17 Workshop is an important step towards developing a community around translational data science. We will work together as a group to start writing a white paper on translational data science at the workshop and finish it within the following six to eight weeks.
National Science Foundation funding pending.
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i) What is translational data science?
ii) What are some success stories in translational data science?
iii) What are some models for translation that have proved successful?
iv) What are some research challenges, opportunities, and high priority areas in translational data science?
v) How can we teach translational data science?
This workshop is by invitation only. Participants will be asked to submit a one to two-page position paper about their views on translational data science as a whole or related to one of these four themes.
University of California at San Diego
Michigan State University
University of Chicago
Robert L. Grossman
University of Chicago (Co-Chair)
University of Washington
The Ohio State University (Co-Chair)
University of Illinois at Chicago