When Phan Anh (Tom) Nguyen took the Computing for Good class in the fall of 2016, the School of Computer Science (SCS) master’s student knew he wanted to continue the work of the class well past the final project.
“I got a scholarship that funded my stay over here, so I thought it would be great if I could use my degree to make the world a better place,” Nguyen said. “I saw Computing for Good in the course catalogue and thought, ‘This must be it. This is a good place to start!’”
The Computing for Good Mission
Computing for Good was started in 2008 to use computer science to improve the human condition. Students were partnered with local nonprofits to develop software, data analysis, and other tools to benefit the community. Despite the noble goal, the nonprofits could only achieve so much progress during a four-month class.
During his class, Nguyen helped build project management software for United Way. Although he appreciated the impact of this project, he wanted to push the Computing for Good mission further and work with one of its founders more, SCS Professor Ellen Zegura.
Around the same time, Nguyen started attending events hosted by Serve-Learn-Sustain (SLS), a Tech-wide educational initiative on community sustainability. When SLS was looking for a computer science student to help develop computing tools professors could integrate into their classes, Nguyen jumped on it.
Creating the SLS Toolkit
The SLS Toolkit is a collection of guides and assignments that enable instructors to incorporate sustainability into their courses. SLS develops tools by determining what would be the most useful and most likely to be used by professors.
Zegura realized that a lot of computing courses revolve around data because it can be visualized, analyzed, or joined with other sets to generate larger findings. She believed a helpful SLS tool would be data sets professors could build projects around.
“The teaching toolkit is a library of resources to help instructors across disciplines,” Zegura said. “Tom Nguyen has focused specifically on data and sustainable communities to connect to computer science courses.”
Before they started compiling data, Nguyen interviewed five professors to learn how professors might use tools in their class to help the SLS mission.
“Working with professors to incorporate socially reflective questions in their class is too big of a jump,” Nguyen said. “You really have to work step by step and find data sets the students will be interested in and the professors will want to teach.”
Over the summer of 2017, Nguyen worked on three data sets:
- Data on vacant lots that could be converted into urban farms
- One year of data of how and when people use their MARTA Breeze card
- Where and when New Yorkers saw rodents to be extrapolated to a rodent infestation problem in Atlanta west side neighborhoods
Each data set is large and dynamic enough to interest professors and their students, but also socially focused enough to promote the SLS intention. The urban farming set is really about sourcing adequate food, the MARTA data set is about strengthening transportation infrastructure, and the rodent data set could be applied to improving living in communities close to Tech.
The data sets were rolled out at the end of the fall 2017 semester and are already being integrated into the syllabus. Director of the Division of Computing Instruction and Senior Lecturer Monica Sweat used the MARTA set in her database class.
For Nguyen, working with SLS is about more than just creating tools; it has changed his approach to computing. As a machine learning student, the focus is often on using systems to assist doctors or policy makers, but the communities next door need help, too, and not from cutting-edge tech.
“SLS completely opened up my mind about the role technology can play in our lives,” he said. “Instead of pushing technology on communities, the way I look at the world is to find the problem first and use stable technologies that can provide the necessary role in people’s lives.”