Name: Sarah Brown
URI Title: Assistant Professor of Computer Science
Assistant Professor of Computer Science Sarah Brown is a bit of a traveler. After receiving her Ph.D. in Electrical Engineering from Northeastern University in December 2016, Brown went west, serving as a Chancellor’s Postdoctoral Fellow at the University of California Berkeley from January 2017 to July 2018. From there, she returned to New England in August 2018, this time serving as a Data Science Postdoctoral Research Associate at Brown University until July 2020, after which she took up her position as one of URI’s newest faculty members. “I had always liked visiting RI, and I fell in love with living here when I started working at Brown as a Postdoc,” she says. “Little Rhody is quirky in a way, and I’m excited to support diversity in computer science, which is severely lacking, by working at a public institution. In my interview, the faculty in Computer Science and Statistics were friendly and welcoming, and that was something I knew I wanted when I started my career as a professor.”
This semester, Brown is teaching CSC 310: Programming for Data Science, and next semester she looks forward to teaching CSC 592: Machine Learning for Science & Society. When she isn’t teaching, Brown looks forward to continuing to pursue her research interests. “My research is about how we can adapt machine learning algorithms and the systems they’re embedded in in order to prevent AI from reinforcing patterns of discrimination,” she says. “Previously, programmers directly wrote all of the instructions in the algorithms that were deployed, but machine learning allows them to define general goals and describe how to learn from the world, feed their algorithm a bunch of data, and let that algorithm write another algorithm. These computer generated algorithms require more careful examination in order for them to not learn the wrong things about the world. I’m currently looking at how we can build tools for people with social science expertise instead of computer science expertise to examine the computer-written algorithms. In another project, I’m working to develop better learning algorithms that can learn from general data and specific expert advice at the same time. My third main project right now is in collaboration with a social psychologist at Brown University, trying to understand what types of fairness people would prefer algorithms respect, what social factors influence their preferences, and what features of an algorithm influence their preferences.”
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Thinking back on her own prior collegiate experience — and now with almost a semester as an Assistant Professor under her belt — Brown has plenty of advice to give, stressing the importance of a balanced course-load. “My advice for computer science students is to take advantage of a liberal arts education to gain an understanding of things a computer science degree doesn’t require,” she says. “Increasingly the hardest problems in this field aren’t the purely computational ones, but the aspects of how we make this real in a safe, fair, trustworthy way. That requires understanding from the social sciences, humanities, and experimental practices of physical sciences. Computing touches every aspect of our daily lives now, so we, as computing professionals, need to be aware of when problems are harder than they appear and able to ask good questions and gather advice from other experts.” She even has words of wisdom for those not involved in computer science, as she advises, “For non-computer science students, I have complementary advice: to take at least one course that helps you understand a little bit about how computers and programming work and then really think about how it intersects with your other material. The world is changing quickly, and you have a tremendous advantage to adapt and be creative if you can relate with technology not only as a tool, but anticipate how it will impact your domain.”
~Written by Chase Hoffman, Writing & Rhetoric and Anthropology Double Major, URI Class of December 2020