Are you a scientist who wants to take a multidisciplinary, team approach to solving an important societal problem? Can you move quickly, think like an entrepreneur, and thrive under a short leash? Then the National Science Foundation (NSF) in Alexandria, Virginia, has a new funding program that might be a good fit.
The novel initiative, which next week has its first deadline for two-page preliminary proposals, goes by the hokey sounding name Convergence Accelerator (C-Accel) pilot. But NSF is dead serious about the funding. By the end of this summer, the agency envisions awarding up to $1 million each to 50 teams for 9-month pilot projects. Those pilots will then compete for a smaller number of $5 million awards extending into 2022.
NSF Director France Córdova has set aside $60 million this year for C-Accel and has requested an additional $60 million in 2020, with the hope that phase two teams will attract at least $40 million in total from other sources.
Agency officials expect each applicant to address one of three real-world challenges: manipulating large and nonproprietary data sets to build what NSF calls open knowledge networks, helping people get the training they need for good jobs, and finding better ways of matching employers with suitable candidates. But instead of spinning off a company to sell a product, grantees are supposed to develop tools and approaches that will help other researchers working on those problems.
“We have other programs that focus on technology transfer,” explains Jim Kurose, head of NSF’s Computer and Information Science and Engineering (CISE) directorate. “But C-Accel is about making available tools that the research community is going to use, whether in academia or industry.”
“We haven’t lost sight of our mission to fund basic research,” adds C. Suzanne Iacono, head of NSF’s Office of Integrative Activities, where C-Accel will be housed. “But we want to accelerate the transition of that research into practice. … This is really something new for NSF.”
Build, test, revise, repeat
Divya Srinivasan, an assistant professor of engineering at Virginia Polytechnic Institute and State University in Blacksburg, already has a standard NSF grant that hints at the kinds of projects C-Accel envisions supporting. Last year, the agency gave her and a multidisciplinary team nearly $3 million over 5 years to develop a whole-body exoskeleton that could make people more productive at work. The idea is to use the technology to augment workers’ skills rather than to take away their jobs.
“Let’s say a factory with robots is willing to open up a part of its factory floor,” Iacono posits. “The idea is to apply what the researchers have developed, like an exoskeleton, and see if it actually works. That’s what a living lab is. You collect data, compare it to your metrics and goals, and then go back to campus and improve the tool. It’s called spiral design.”
Srinivasan says her team is likely to take the plunge and apply for a C-Accel pilot. NSF officials hopes hundreds of groups will do likewise, including social scientists. “It could be a new way of presenting information, a new way of organizing people, or a new support system,” says Sara Kiesler, a program manager in NSF’s Division of Social and Economic Sciences. “Our core programs address important problems. But with C-Accel we’re also expecting a deliverable.”
Some C-Accel applicants, like Srinivasan, may build on research that NSF is already funding as part of its 10 Big Ideas initiative, which Córdova launched in 2016. C-Accel’s focus on open knowledge networks aligns, for example, with the Big Ideas emphasis on “harnessing big data.” And C-Accel’s two other tracks relate to another Big Idea, the Future of Work at the Human-Technology Frontier, which is helping support Srinivasan.
Spell it out
C-Accel’s emphasis on a tangible end product isn’t the only thing that sets it apart from many other NSF programs; it will also pursue a distinctly different approach to grantmaking. Some have likened it to the approach used by the Defense Advanced Research Projects Agency within the Department of Defense, in which program managers set clear research milestones and hold grantees accountable for meeting them. “The deliverables are clear, so our management can be more directed,” Kurose says. “The evaluation will also be more mission-driven than the standard NSF grant.”
Researchers interested in C-Accel funding should submit their brief outline by 15 April (although NSF says it may consider proposals submitted after that date). Scientists should not only describe their idea, but also provide a list of team members, what each is expected to contribute, and what the group hopes to accomplish by the end of the project.
Applicants with proposals deemed to fit the criteria will be invited to submit a full, 15-page proposal, due 3 June, and given some guidance. Those who fall short will simply be told no, Iacono says.
The full proposals will get an expedited review, first by NSF program managers and then by outside experts. Winners will be notified sometime in July.
“Yes, it does sound like an aggressive schedule,” says Jeremy Epstein of CISE. “But I come from a world of nonacademic research, where this would be a very normal time frame.”
Make a pitch
Those who receive phase one awards will become part of a cohort that will participate in several group activities, both in-person and virtually, over the duration of the grant. The training will include cross-cultural team building exercises, customer identification and market analysis, and guidance on how to pitch their research to potential industry partners, venture capital investors, and users. Those pitches, to be presented next winter, will help determine who receives a phase two award in the spring of 2020.
That competition will be open only to those already being supported. But NSF expects to fund additional C-Accel cohorts if the pilots are successful, Iacono says, and this spring it will ask the community to submit ideas for future tracks. Those themes may not be aligned with any of NSF’s Big Ideas, she adds. “We started with [the current Big Ideas],” she notes, “because they seemed the ripest for generating new research tools.”