Hullman Receives Microsoft Research Faculty Fellowship

Jessica Hullman, Breed Junior Professor of Design and assistant professor of computer science and journalism, received a 2019 Microsoft Research Faculty Fellowship which recognizes innovative, promising new faculty members with $100,000 annually for two years to pursue breakthrough, high-impact research.

Hullman will use the grant for research in the development of computational tools to improve how people reason with and make decisions from data, with a focus on uncertainty representation through interactive visual interfaces that enable users to articulate and reason about prior beliefs.

“I’m trying to push the boundaries of how we represent and support reasoning about uncertainty in data with my work,” she said. “I think too often we build interactive data analysis and visualization systems that are agnostic to a user’s prior knowledge and preferences, and this prevents a system from actually building on or enhancing inferences about data.”

The grant provides five fellows with freedom to plan their research, hire graduate students, build labs, and acquire equipment. The researchers were chosen through a rigorous, multi-tier selection process that involved 22 reviewers. The reviewers looked for future leaders at the beginning of their careers with not only the capability to pursue cutting-edge research, but also the skills that are necessary to bring those ideas to fruition and to communicate complex concepts.

“The Microsoft Research Faculty Fellowship will give my students and me more freedom to explore how our ideas around uncertainty visualization and reasoning can be useful in other domains,” Hullman said. “I see exciting new research opportunities related to supporting understanding of statistical model results and technology-aided decision making under uncertainty. I’m excited to use the fellowship to expand our thinking to problems like machine learning model interpretability and bias, education, and medical decision making, to name a few.”

Hullman’s research focuses on helping more people make sense of complex information and to reason about uncertainty. She uses controlled experiments to identify and model how people reason with data and uncertainty, and she also creates novel interactive tools and techniques to extend and amplify users' abilities to think with data by aligning with their internal representations of complex phenomena.