NSF 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 was an important step towards developing a community around translational data science.


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

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 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. 

Organizing Committee

Chaitan Baru
University of California at San Diego

Alan Blatecky
RTI International

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