Enabling AI Innovation via Data and Model Sharing: An Overview of the Nsf Convergence Accelerator Track D

Authors

  • Chaitanya Baru National Science Foundation
  • Michael Pozmantier National Science Foundation
  • Ilkay Altintas University of California San Diego
  • Stephen Baek University of Virginia
  • Jonathan Cohen Princeton University
  • Laura Condon University of Arizona
  • Giulia Fanti Carnegie Mellon University
  • Raul Castro Fernandez University of Chicago
  • Ethan Jackson Microsoft Corporation
  • Upmanu Lall Columbia University
  • Bennett Landman Vanderbilt University
  • Hai Li Duke University
  • Claudia Marin Howard University
  • Beatriz Martinez-Lopez University of California Davis
  • Dimitris Metaxas Rutgers University
  • Bradley Olsen MIT
  • Grier Page RTI
  • Yelda Turkan Oregon State University
  • Jingbo Zhang University of California San Diego
  • Peng Zhang Stony Brook University

DOI:

https://doi.org/10.1002/aaai.12042

Abstract

This article provides a brief overview of 18 projects funded in Track D—Data and Model Sharing to Enable AI Innovation—of the 2020 Cohort of the National Science Foundation's (NSF) Convergence Accelerator (CA) program. The NSF CA is focused on transitioning research to practice for societal impact. The projects described here were funded for one year in phase I of the program, beginning September 2020. Their focus is on delivering tools, technologies, and techniques to assist in sharing data as well as data-driven models to enable AI innovation. A broad range of domain areas is covered by the funded efforts, spanning across healthcare and medicine, to climate change and disaster, and civil/built infrastructure. The projects are addressing sharing of open as well as sensitive/private data. In September 2021, six of the eighteen projects described here were selected for phase II of the program, as noted in this article.

The NSF Convergence Accelerator, by James Gary

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Published

2022-03-31

How to Cite

Baru, C., Pozmantier, M. ., Altintas, I. ., Baek, S. ., Cohen, J. ., Condon, L. ., Fanti, G. ., Fernandez, R. ., Jackson, E. ., Lall, U. ., Landman, B. ., Li, H. ., Marin, C. ., Martinez-Lopez, B. ., Metaxas, D. ., Olsen, B. ., Page, G. ., Turkan, Y. ., Zhang, J. ., & Zhang, P. (2022). Enabling AI Innovation via Data and Model Sharing: An Overview of the Nsf Convergence Accelerator Track D. AI Magazine, 43(1), 93-104. https://doi.org/10.1002/aaai.12042

Issue

Vol. 43 No. 1: Spring 2022

Section

Special Topic Articles

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