Paninski’s group develops statistical methodology and theory for analyzing neural data, and has a particular interest in understanding information encoding and decoding by large neural populations, with expertise in large retinal data, calcium imaging video data and neural prosthetics research. Other interests include methodologies for estimating connectivity in neuronal networks, for optimal experimental design, for efficient inference in high-dimensional state-space models and for fast Monte Carlo methods.