The National Science Foundation (NSF) recently announced $17.7 million in funding for 12 Transdisciplinary Research in Principles of Data Science (TRIPODS) projects, which will bring together the statistics, mathematics and theoretical computer science communities to develop the foundations of data science.

Technological advances and unprecedented access to computing infrastructure have resulted in an explosion of data from different sources. The availability of these data — their volume and variety, and the speed at which they are collected — is transforming research in all fields of science and engineering. TRIPODS awards will enable data-driven discovery through major investments in state-of-the-art mathematical and statistical tools, better data mining and machine learning approaches, enhanced visualization capabilities and more.

The TRIPODS Phase I awards recently announced will support the development of small collaborative institutes. A future TRIPODS Phase II is planned to support a smaller number of larger institutes. Phase II will select awardees through a second competitive proposal process from among the Phase I institutes, as well as any new collaborative partners Phase I awardees bring on board.

Some of the award titles, principal investigators, and institutions for the TRIPODS Phase I projects are listed below:

• UA-TRIPODS: Building Theoretical Foundations for Data Sciences: Hao Zhang, University of Arizona
• Foundations of Model Driven Discovery from Massive Data: Jeffery Brock, Brown University (Convergence and EPSCoR co-funding)
• Berkeley Institute on the Foundations of Data Analysis: Michael Mahoney, University of California, Berkeley
• TRIPODS: Towards a Unified Theory of Structure, Incompleteness and Uncertainty in Heterogeneous Graphs: Lise Getoor, University of California, Santa Cruz
• From Foundations to Practice of Data Science and Back: John Wright, Columbia University
• TRIPODS: Data Science for Improved Decision-Making: Learning in the Context of Uncertainty, Causality, Privacy, and Network Structures: Kilian Weinberger, Cornell University (Convergence co-funding)
• Transdisciplinary Research Institute for Advancing Data Science (TRIAD): Xiaoming Huo, Georgia Institute of Technology
• Institute for Foundations of Data Science (IFDS): Piotr Indyk, Massachusetts Institute of Technology
• Topology, Geometry, and Data Analysis (TGDA@OSU): Discovering Structure, Shape, and Dynamics in Data: Tamal Dey, The Ohio State University
• Algorithms for Data Science: Complexity, Scalability, and Robustness: Sham Kakade, University of Washington
• Institute for Foundations of Data Science: Stephen Wright, University of Wisconsin-Madison (Convergence co-funding)

To learn more about the TRIPODS, see the NSF news release.