I am a postdoctoral researcher at the Princeton Neuroscience Institute, working with Nathaniel Daw. My research focuses on how we learn predictive representations, and how and when we rely on those representations in planning, and is guided by behavior, imaging, and computational modeling. I am particularly interested in how we build multi-step predictive models via simple observations such as co-occurrence, how these predictive models can capture high-level structure of the environment, and how people and animals dynamically adapt their decision making, relying on optimized predictive models when possible, and costly mental simulation when necessary.
To do so, I seek to characterize the processes that allow people to learn predictive structures, showing how properties of those structures such as modularity or clustering can reveal high-level graph-like regularities, and measuring adaptive reliance on predictive models that build upon these regularities. My research utilizes advances in network science, control theory, and reinforcement learning, alongside behavior and neuroimaging to study how the brain supports complex planning and decision making over temporally-extended sequences of events.