Joshua Detwiler, a Ph.D. student in computer science, believes that his research interest in solving automated redistricting/gerrymandering as an optimization problem is well aligned with both the applied and interdisciplinary focus at the Discovery Analytics Center and with what he is learning as a research trainee in the National Science Foundation-sponsored urban computing graduate certificate program, administered through DAC.
“I have always wanted to work on applied research that would help solve a large real-world problem,” said Detwiler, who is advised by Layne Watson, a professor in the departments of computer science, mathematics, and aerospace and ocean engineering. “Gerrymandering is both a solution and problem to real world-political redistricting, school redistricting, and other similar applications.”
In political redistricting, he said, it answers questions
such as how to represent a population in the U.S. House of Representatives,
where multiple representatives are elected for each state. In school
redistricting, neighborhood-like areas are assigned to city or county schools
to utilize school capacities and address other concerns like having diversity in schools.
“However,” said Detwiler, “gerrymandering is often under scrutiny as a historically manual process where individuals might draw boundaries to serve a different agenda, My research aims to bridge some of the current gaps in numerical optimization software that will also better solve for an optimal redistricting plan, namely by tackling computationally difficult constraints like the connectivity of district constituents.”
Detwiler earned bachelor degrees in both computer science and mathematics from Virginia Tech in December of 2019. During his last two summers as an undergraduate, he interned at the U.S. Department of the Navy where he worked on automating a cybersecurity benchmark for the Red Hat Linux Entry-level Server 4 (RHEL 4) operating system. He also wrote unit tests and user interface (UI) tests for Java components in legacy software and successfully presented automated testing principles to the software team to change our organization culture that had absence of testing.
This past summer, Detwiler returned to the Navy as an intern, working remotely due to COVID-19. He worked on a distributed application that could provide application layer network performance metrics.
He is projected to graduate in 2024. His career goal lies in academia, where he can combine teaching and research as a professor.