Two Tufts Researchers in National Science Foundation Big Ideas Competition
When the National Science Foundation put out a call last fall for big ideas to inform its research agenda for the coming decade, it received more than 800 submissions. NSF staff winnowed that number down to thirty-three this spring, and two researchers from Tufts made the cut: biologist Michael Levin and computer scientist Matthias Scheutz.
The competition, named NSF 2026 Idea Machine—that year being the 250th anniversary of the founding of the country—will “inform the U.S. agenda for fundamental science, engineering, and STEM education research,” according to the NSF.
Each of the thirty-three semifinalists submitted a short pitch video for a research challenge they think deserves government funding. The videos are available and open for public comment until June 26. Around a dozen finalists will be interviewed by a blue-ribbon panel of experts this summer, and up to four grand prize winners will be named by September.
Levin, A92, Vannevar Bush Professor of Biology and director of the Allen Discovery Center at Tufts, makes the case for investigating how individual cells harness the laws of physics to form complex functional bodies, and how cellular information-processing and signaling machinery work together to build and repair bodies.
“We’re very excited to have our ideas selected and profiled by the NSF in this program,” said Levin. “Understanding cellular decision-making in the context of anatomical construction and repair is a fundamental scientific goal. It will have implications from evolutionary biology to regenerative medicine to robotics and AI. We’re thrilled to have the opportunity to explore this exciting emerging field and share it with viewers.” (See Levin’s video and learn more here.)
Scheutz, who is also director of the Human-Robot Interaction Lab at Tufts, and Vasanth Sarathy, a doctoral candidate in computer science and cognitive science, wants to focus on the question, What can machines invent and how? When faced with new problems, humans can improvise to come up with solutions, inventing new ways of using existing materials. Can machines do that, too?
“Problem solving was one of the original goals of work in AI and it fell by the wayside over the last few decades,” said Scheutz. “Yet, problem solving—and in particular creative problem solving—is at the heart of human intelligence and it is what we need to solve our increasingly urgent global problems.”
It’s unlikely, he said, that humans will devise quick solutions to issues like climate change and wealth inequality. “We need efficient, workable solutions fast, and if we cannot do it ourselves, we still have the option to build machines that can do it for us, and possibly better than any human could.”
At the core of this idea “is the skill of creative problem solving, which humans possess in abundance—the ability to get out of sticky situations and generate elegant solutions to difficult problems,” said Sarathy. “We would have AI that can help us cure disease by actively suggesting new experiments to perform—and help us tackle large-scale problems in science and society that even we, humans, have not been able to solve.” (See Scheutz’s and Sarathy’s video and learn more here.)
Taylor McNeil can be reached at firstname.lastname@example.org.