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People increasingly rely on visual representations of information to explore and make sense of data. However, people have inherent biases that often lead to errors and inefficiencies in the decision making process. The goal of our research is to help people make better decisions while exploring and analyzing data using visualizations. We introduce computational methods for quantifying an analyst’s biases based on their interactions in the visualization. Using that information, we illustrate ways to modify or design new visualization systems that mitigate biased decision making.

A multi-agent AI that incorporates elements of the human visual perception theory and is capable of co-creating non-representational art with a human collaborator on the web.

The StudentLife project has been developing ways to combine multiple streams of data about student habits into meaningful holistic analyses of individual's well-being. Communicating those results to a student population poses a challenge to provide information in a legible form and provide meaningful and helpful, and importantly not harmful, feedback to enable students to improve their well-being.