I am a postdoctoral researcher at Princeton University applying theory- and data-driven approaches to problems in neuroscience and behavior. I work in the Princeton Neuroscience Institute and the Center for the Physics of Biological Function.

I am interested in how the brain processes the complex streams of sensory inputs we experience in day-to-day life. In particular, how are such inputs integrated over time to support memory-based computational, learning, and behavioral processes? I am also interested in how macroscopic computations emerge from strongly connected microscopic processes. For instance, how can biological neural network models simultaneously produce brain-like chaos while also robustly retaining information in working memory? To address these questions I combine ideas and tools from a range of disciplines including dynamical systems theory, statistical physics, time-series analysis, coding theory, and high-dimensional computing.

I have been a teaching assistant for several theoretical neuroscience and related courses, including the Allen Institute’s Summer Workshop on the Dynamic Brain, the IBRO-Simons Computational Neuroscience Imbizo, and Coursera’s Computational Neuroscience course.

Check out some of the online tutorials I’ve made or my tips & tricks for improving your computational research experience.