His interests span a wide set of topics in mathematical neuroscience; current and recent projects focus on timing and decision making in idealized neural network models and on basic properties of correlations and reliability in spiking neural circuits. He and his colleagues are working to bridge the gap between these scales of modeling. Before UW, Shea-Brown was a postdoctoral fellow at NYU's Courant Institute and Center for Neural Science, and was mentored by John Rinzel. In 2004, he completed his PhD in Princeton's Program in Applied and Computational Mathematics where Phil Holmes and Jonathan Cohen advised him. They worked on stochastic neural network models, asking how they might be controlled to explain fascinating data from brain recordings and task performance.