Ashlynn Crisp is one of just 33 doctoral students in the country to land a top computational science fellowship. The math sciences graduate student will get special training to apply high-performance computing to a range of fields.

“This fellowship is a fantastic opportunity for me because it provides me with funding, hands-on experience at a national lab, and networking opportunities with leaders in the computational sciences to support my academic and professional growth,” she said.

The Department of Energy Computational Science Graduate Fellowship, renewable for up to four years, provides participants with a yearly stipend, full payment of university tuition and fees, an annual academic allowance and a 3-month practicum at one of 21 DOE laboratories or sites across the country.

Ashlynn started at PSU as a transfer student from Portland Community College, graduating in 2021 with a bachelor’s in mathematics. 

“As an undergraduate, I always enjoyed applying what I was learning in my classes to real-world problems,” she said. 

Through working as an undergraduate research assistant and completing her honors thesis, she says she found research to be engaging and fulfilling, ultimately deciding to pursue her Ph.D.

“In my opinion, the best part of graduate school is working with my peers and professors. I would not be in a Ph.D. program — and certainly would not have applied for this fellowship —  without the encouragement and support from my professors and classmates. I would encourage other students to take advantage of office hours and study groups. Getting to know the people in the program is one of the most rewarding aspects of the experience.

Ashlynn Crisp

“I really appreciated the faculty at PSU and knew that the Mathematical Sciences Ph.D. program was a great fit for me because it has the resources to support my research and professors I wanted to work with who have expertise in my area of interest,” she said.

Wanting to stay in Portland and being close to family and friends helped seal the deal. She says she’s particularly interested in statistics and machine learning because these fields provide tools to model complex problems and can be applied in many fields, such as chemistry, physics, medicine and climate science.

“My focus is on developing scalable machine learning and statistical models that are more user-friendly and accessible for users from a variety of backgrounds,” she said.

This summer, she’ll get more experience with machine learning and computing while doing a data science internship with the Lawrence Livermore National Laboratory in California.

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