The Brown School’s cross-campus collaboration for a unique PhD program combining computers, data and social sciences has existed for just a few years, but is already showing great promise in attracting top-tier students focused on innovative solutions to society’s thorniest problems.
“The response has been incredible,” said Patrick Fowler, associate professor and director of the doctoral program in public health sciences at the Brown School and co-director of the Division of Computational & Data Sciences (DCDS). “We’re getting highly competitive pools of applicants and an extremely good acceptance yield – we’re getting our top picks.”
Along with the departments of computer science, political science, and psychology, the program is for PhD students interested in the uses of data-centric methods for addressing social science questions. Public Health/Social Work is one of four tracks; students apply computational approaches to a broad range of questions, such as homelessness and health disparities (the other tracks are computational methodologies, political science, and psychological/brain sciences). The 15 students currently enrolled come from either a computer science or social science background. Co-mentors from different disciplines advise students toward a degree in computational data sciences. A master’s degree is not required to enter the program.
Fowler said the popularity of the program is grounded in its increasing real-world value. “Having a strong foundation in data methods and their social applications is becoming increasingly needed and appealing to students,” he said. “The technology is exploding and creating new opportunities to improve lives.” The program also focuses on the unintended consequences of using new technology. “We’ve got machines making decisions for better or worse,” he said. “Students need to know how these methods are working and their social implications.”
Joshua Landman is a fourth-year student in the program, with only his dissertation left to complete. He’s aiming to build clinical risk-prediction models for hospital admission and mortality rates in the context of COVID-19. He came to the program with a master’s degree in computer science, but decided to focus on public health only after taking a required first-year course in theoretical orientation to public health sciences taught by the Brown School’s Darrell Hudson and Douglas Luke, and Christine Ekenga, now an assistant professor at Emory University’s Rollins School of Public Health.
“Taking that course clarified and shaped how I’ve continued to think about my work,” he said. “I want to find out what drives the creation of this data to inform what we learn when we analyze it.”
“Data is one of the best ways for us to make informed decisions about problems we’re facing, for individuals or societies,” Landman said. “To be able to intelligently reason about this data and how confident we are is incredibly important, especially in health care and public health. DCDS is training us to be, if not subject-matter experts, the next closest thing, and providing an opportunity to collaborate with all of the relevant experts in that area.” He is working with the Institute for Informatics at the School of Medicine.
Landman said the program is special. “It’s not every data science program that lets you pick a field and work with world-renowned experts in it,” he said. “It’s a privilege to be able to do that, and it offers you opportunities for so much more than just working with data; you get a deeper understanding within that area.”
Another student with a public health interest is Abigail Lewis, who came to the program after graduating from Grinnell College and is now in her second year. She’s interested in using data science to understand disparities in access to health care and inpatient outcomes. She’s currently working in the Institute for Informatics to understand the use of biomarker testing in Alzheimer’s diagnoses and racial disparities in access to the tests.
Lewis was drawn to the program by its focus on applied data, as well as the location in St. Louis, her hometown. She said the newness of the program and its wide reach have posed challenges — and opportunities.
“DCDS doesn’t exist anywhere, but it exists everywhere,” she said. “Being in the second cohort, there’s not a lot of precedent for anything, but that has provided more flexibility for us and we’ve had a lot of opportunities to give feedback and shape the program in the future. I definitely would recommend it to students interested in the application of data science. It’s a great program and will improve as they work out the kinks. I have not seen a program like this anywhere else.”
Fowler agreed that the breadth and innovative nature of the program has required flexibility and adjustments as it advances. “We’re walking the walk of transdisciplinary research,” he said. “We’re working at the intersections where problems need to be solved. It’s great that students are the bridge. We thought that by bringing in students with innovative ideas, they’d make innovative connections, and that has been the experience so far.”
Students are engaged in meaningful research. PhD candidate Amanda Kube is studying ways to minimize biases in homeless service delivery. Second-year student Alex DiChristofano is working with a team from the University of Pittsburgh to design a web platform that increases online support for Black-owned businesses from the university’s students, staff, and faculty. The project recently won the New Horizons Award at the Association for Computing Machinery. Ivy Smith, a first-year DCDS student, received the inaugural Center for the Study of Race, Ethnicity & Equity graduate fellowship.
“This program is going, and it’s going gangbusters,” Fowler said.