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.


June 26 - 27, 2017


Gordon Center for
Integrative Data Science
3rd Floor, Room W301/303
929 E. 57th Street
Chicago, IL 60637



Sponsored by:

University of Chicago
Ohio State University

National Science Foundation funding pending.

Interested in sponsoring this event?

Contact us at:

Workshop Themes

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 challenges, opportunities, and high priority areas in 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. 

Organizing Committee

Chaitan Baru
University of California at San Diego

Rachel Croson
Michigan State University

Michael Franklin
University of Chicago

Robert L. Grossman
University of Chicago (Co-Chair)

Bill Howe
University of Washington

Raghu Machiraju
The Ohio State University (Co-Chair)

Elena Zheleva
University of Illinois at Chicago