Naturally introduced to a group of engineers, Nina Andrejevic cherished making drawings of her home and different structures while a youngster in Serbia. She and her twin sister shared this energy, alongside a craving for math and science. Over the long run, these interests joined into an insightful way that imparts a few credits to the family calling, as indicated by Andrejevic, a doctoral applicant in materials science and designing at MIT.
“Engineering is both an inventive and specialized field, where you attempt to streamline highlights you need for specific sorts of usefulness, similar to the size of a structure, or the design of various rooms in a home,” she says. Andrejevic’s work in AI looks like that of engineers, she accepts: “We start from a vacant site – a numerical model that has arbitrary boundaries – and our objective is to prepare this model, called a neural organization, to have the usefulness we want.”
Andrejevic is a doctoral advisee of Mingda Li, an associate teacher in the Department of Nuclear Science and Engineering. As an exploration colleague in Li’s Quantum Measurement Group, she is preparing her AI models to chase after new and helpful qualities in materials. Her work with the lab has arrived in such significant diaries as Nature Communications, Advanced Science, Physical Review Letters, and Nano Letters.
Nina and Jovana Andrejević
MIT doctoral competitor Nina Andrejević (right) has created with her twin sister Jovana (left), a PhD applicant at Harvard University, a technique for testing material examples to foresee the presence of topological qualities that is quicker and more flexible than different strategies. Credit: Gretchen Ertl
One area of extraordinary interest to her gathering is that of topological materials. “These materials are an extraordinary period of issue that can ship electrons on a superficial level without energy misfortune,” she says. “This makes them exceptionally intriguing for making more energy-effective innovations.”
With her sister Jovana, a doctoral competitor in applied physical science at Harvard University, Andrejevic has fostered a strategy for testing material examples to foresee the presence of topological qualities that is quicker and more adaptable than different techniques.
Assuming a definitive objective is “delivering better-performing, energy-saving advancements,” she says, “we should initially know which materials make great possibility for these applications, and that is something our examination can help affirm.”