NSF Graduate Research Fellowships Awarded to Additional Alums

Three alumni of The University of Texas at Dallas received National Science Foundation (NSF) Graduate Research Fellowships this summer to advance their studies of machine learning, data science and protein design.

Osayamen Jonathan Aimuyo BS’23, Purvi Contractor BS’23 and Jie Chen BS’25 became the latest recipients when additional funding was provided to the program in June. That brought the total awarded this year from 1,000 to 1,500. Four Comets were awarded fellowships earlier this year.

The program provides tuition support and a three-year stipend for students pursuing graduate studies in science, technology, engineering and mathematics.

Aimuyo, a Nigerian immigrant who earned his UT Dallas degree in computer engineering, said he is eager to continue his research in machine learning as he pursues a PhD at Stanford University this fall.

“Machine learning at the moment is a very ripe field for research,” he said. “There’s a lot of interesting research problems we can solve.”

Aimuyo earned an associate degree with Phi Theta Kappa honors in 2019 from Dallas College Brookhaven Campus. A UT Dallas Presidential Achievement Scholarship recipient, he graduated summa cum laude from UTD in 2023 with Tau Beta Pi and Phi Kappa Phi honors.

The first in his family to pursue a graduate degree, Aimuyo said he spent his summers while an undergraduate interning as a software engineer at Microsoft, Chime Financial Inc. and JPMorgan Chase & Co. to pay for his education.

That led him to Cornell University, where his master’s thesis research introduced him to the field of machine learning systems.

Aimuyo’s research focuses on how computers involved in machine learning can communicate more quickly and efficiently with each other. Large machine learning accelerators — central processing units that speed up calculations — can process data quickly, he said. But when it comes to interfacing, or communicating, with other accelerators, the information transfer lags, like a traffic jam.

“It’s not just about making the highway bigger, but about directing traffic systematically so cars don’t collide, stall or get stuck in traffic jams,” he said. “My work develops the algorithms and systems that keep things moving smoothly and efficiently.”

When Contractor was in elementary school, she remembers being so excited about prime numbers that she wrote them down in a list to try to find a pattern. While the experience did not reveal mathematical order, it led her to discover an affinity for math.

Contractor, who graduated cum laude in mathematics with a concentration in applied math, plans to continue at UTD to pursue her doctoral degree, at which time she will hold a Eugene McDermott Graduate Fellowship.

“I knew this is the place I wanted to go for my PhD,” she said. “The bachelor’s in math gave me a good balance between theoretical and applied math courses.”

As an undergraduate, Contractor participated in a research project that she presented at the 2022 Summer Platform for Undergraduate Research Symposium at UTD and the 18th University of North Carolina Greensboro Regional Mathematics and Statistics Conference.

“This experience really illuminated to me the inner workings of machine learning algorithms and the role of mathematics in machine learning,” she said, adding that the experience also allowed her to learn technical writing and improve her public speaking.

She also volunteered with the Freshman Mentor Program and the First-Year Leader Program, where she taught freshman seminars.

Chen, a computer science graduate, was awarded the NSF fellowship to support her research on developing machine learning models for designing proteins. She is pursuing a PhD in computer science at the University of Washington.