About TDAI

About TDAI

Fun Facts

What We Do

Ohio State Translational Data Analytics Institute brings together university faculty and students with industry and community partners to create data science and analytics solutions to grand challenges for the greater good. We do this by:

  • Advancing research agendas around specific topics and societal challenges through interdisciplinary communities of practice and team science
  • Providing essential resources such as collaborative work spaces; research pilot awards; and consultations on data science and analytics services, training and resources throughout the university
  • Hosting forums, seminars and other events that foster new research ideas and collaborations
  • Connecting industry partners with talented data science and analytics students from across the university through innovative internships, events, challenges and research projects
  • Creating a Master's in Translational Data Analytics for mid-career professionals that responds to industry needs, and a summer camp for high school-age girls interested in data science and analytics
  • Contributing nationally and internationally to advancing translational data analytics as a field

From data science and analytics foundations to domain-specific applications, TDAI leverages Ohio State's breadth of expertise to answer real-world needs across sectors, including health care, agriculture and the environment, business, law and policy, and more.



TDAI Managing Director Dr. Cathie Smith gives overviews of TDAI at the Academic Data Science Alliance Leadership Summit (above) and Intersections 2019 (below)


Screen shot of the Inersections video splash screen logo

Diversity Statement

Embracing diversity is a core value of TDAI and speaks to our commitment to represent and honor voices and perspectives that enrich our dialogue and encourage an inclusive, nurturing culture. TDAI follows the definition of diversity as outlined by Ohio State’s College of Nursing, which states “diversity as the variety of differences and similarities among people which can include gender, race/ethnicity, tribal/indigenous origins, age, culture, generation, religion, class/caste, language, education, geography, nationality, different abilities, sexual orientation, work style, work experience, job role and function, military involvement, thinking style, personality type and other ideologies.”





Why “translational” data analytics?

The use of the term “translational” reflects a fundamental shift toward utilizing data science and analytics in solving issues of global importance. In 2014, TDAI defined “translational data analytics” as the application of data analytics theories and methods to generate solutions for real world problems, or use cases, derived from consultation with impacted stakeholders, and the subsequent delivery and dissemination of those solutions in a manner that enables stakeholders to use them in a tangible and quantifiable way. The National Science Foundation later applied the “translational” concept to data science: “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.

profile photo for Tanya Berger-Wolf

"Translational data analytics" is translating data into knowledge and action to benefit the world.

—TDAI Faculty Director Dr. Tanya Berger-Wolf