Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

Chaitanya Baru, Michael Pozmantier, Ilkay Altintas, Stephen Baek, Jonathan Cohen, Laura Condon, Giulia Fanti, Raul Castro Fernandez, Ethan Jackson, Upmanu Lall, Bennett Landman, Hai Helen Li, Claudia Marin, Beatriz Martinez Lopez, Dimitris Metaxas, Bradley Olsen, Grier Page, Jingbo Shang, Yelda Turkan, Peng Zhang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)93-104
Number of pages12
JournalAI Magazine
Volume43
Issue number1
DOIs
StatePublished - Mar 1 2022

ASJC Scopus subject areas

  • Artificial Intelligence

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