The field of artificial intelligence is moving at a staggering clip, with breakthroughs emerging in labs across MIT. Through the Undergraduate Research Opportunities Program (UROP), undergraduates get to join in. In two years, the MIT Quest for Intelligence has placed 329 students in research projects aimed at pushing the frontiers of computing and artificial intelligence, and using these tools to revolutionize how we study the brain, diagnose and treat disease, and search for new materials with mind-boggling properties.
Rafael Gomez-Bombarelli, an assistant professor in the MIT Department of Materials Science and Engineering, has enlisted several Quest-funded undergraduates in his mission to discover new molecules and materials with the help of AI. “They bring a blue-sky open mind and a lot of energy,” he says. “Through the Quest, we had the chance to connect with students from other majors who probably wouldn’t have thought to reach out.”
Some students stay in a lab for just one semester. Others never leave. Nick Bonaker is now in his third year working with Tamara Broderick, an associate professor in the Department of Electrical Engineering and Computer Science, to develop assistive technology tools for people with severe motor impairments.
“Nick has continually impressed me and our collaborators by picking up tools and ideas so quickly,” she says. “I particularly appreciate his focus on engaging so carefully and thoughtfully with the needs of the motor-impaired community. He has very carefully incorporated feedback from motor-impaired users, our charity collaborators, and other academics.”
This fall, MIT Quest celebrated two years of sponsoring UROP students. We highlight four of our favorite projects from last semester below.
Squeezing more energy from the sun
The price of solar energy is dropping as technology for converting sunlight into energy steadily improves. Solar cells are now close to hitting 50 percent efficiency in lab experiments, but there’s no reason to stop there, says Sean Mann, a sophomore majoring in computer science.
In a UROP project with Giuseppe Romano, a researcher at MIT’s Institute for Soldier Nanotechnologies, Mann is developing a solar cell simulator that would allow deep learning algorithms to systematically find better solar cell designs. Efficiency gains in the past have been made by evaluating new materials and geometries with hundreds of variables. “Traditional ways of exploring new designs is expensive, because simulations only measure the efficiency of that one design,” says Mann. “It doesn’t tell you how to improve it, which means you need either expert knowledge or lots more experiments to improve on it.”
The goal of Mann’s project is to develop a so-called differentiable solar cell simulator that com
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