Statistics spotlight: Susan Glenn, PhD student

Susan Glenn, PhD student in the Department of Statistics

Susan Glenn PhDx’24 has always been fascinated by data and its hidden patterns. As an undergraduate studying mathematics and economics at the University of Washington, Glenn gradually began to realize that statistics was her favorite aspect of both majors.

“Statistics is especially fun because you have this extra piece, which is randomness,” Glenn said. This randomness is inevitable because statisticians investigate real-world datasets—and the real world is rife with uncertainty and unpredictability. 

“You’re trying to understand the unknowable,” Glenn said. “I think that’s the side of statistics I really love.”

Pursuing a PhD

After earning her bachelor’s degree, Glenn served a stint in the Peace Corps, teaching math to children in Lesotho. In 2018, she then returned to the US to pursue an advanced degree in statistics, initially enrolling in the MS Statistics program at UW–Madison. After a year in the program, she was enjoying the experience so much that she switched to the PhD program.

For Glenn, several professors helped spark a newfound interest in theoretical statistics, in addition to the more applied side. She noted Associate Professor Garvesh Raskutti, who she called “an amazing professor,” and Professor Cécile Ané in particular. Both of them, she said, made her “really want to dive deeper into this material and understand the theoretical side of statistics.

At the same time, Glenn has been exploring new and promising methods in applied statistics. One particular approach she has deployed is Topological Data Analysis (TDA), which pulls methods from topography, allowing researchers to look for patterns in noisy datasets by scrutinizing the shape of the data in space—and looking for holes.

An example of a visualization from a study using topological data analysis to detect “cosmic voids,” or vast spaces between structures in the universe. The study was co-authored by Statistics Department Assistant Professor Jessi Cisewski-Kehe, whose lab Glenn works with, in the journal Astronomy and Computing. (From Xu et al., 2019).

“I’ve applied TDA to astronomy problems and imaging problems in biology,” Glenn noted. She said the approach is useful for any scientists who are “trying to detect a pattern in space,” even when those patterns are not directly observable. 

Solving interdisciplinary problems

Working with advisor and Assistant Professor Jessi Cisewski-Kehe, Glenn has explored several applications of statistical methods to astronomy and cosmology. For example, Glenn said that in one project, she has worked alongside scientists at Los Alamos National Laboratory, harnessing statistical methods to “better understand how matter is spread out in the universe,” and “to see how fast it’s moving apart from itself.” This work could help future researchers uncover clues about the true size or age of the universe.

She is also working on a collaborative project to identify gerrymandering, the practice of manipulating legislative maps to give certain groups an undue advantage. Gerrymandering can be hard to detect and thus fly under the radar. Glenn said this is partly because, statistically, “it’s hard to know if the maps you’re looking at are a representative sample of all possible maps. I’m working on ways to get a representative sample.”

In fields from political science to astronomy, data-derived insights are helping Glenn and her colleagues reveal patterns that explain how our country, world, and universe work.

A rewarding experience

In addition to her research, Glenn has also enjoyed interacting with undergraduates as a teaching assistant, helping younger students enjoy—or at least better understand—statistics. She was awarded the Department of Statistics’ Teaching Assistant Award for her outstanding work inspiring undergraduates. 

After she graduates in 2024, Glenn is hoping to secure a postdoctoral fellowship, either in academia or a national laboratory.


Written by: Thomas Jilk, marketing & communications specialist

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