The focus of research in my laboratory is on understanding the structure and function of the visual system. Vision is the most important sense for humans, and damage to vision through disease or stroke is a serious problem affecting millions of people. The human visual system is quite complicated, consisting of several dozen distinct modules (visual areas) arranged in a highly interconnected, hierarchical and parallel network. The visual system is also tightly integrated with other sensory subsystems and systems for memory and language. Because of this interconnectivity, and because the brain is built on modular principles, vision research also has important implications for understanding other brain systems. The goal of the Gallant Lab is to understand the structure and function of the human visual system at a quantitative, computational level, and to build models that accurately predict how the brain will respond during natural vision. Predictive models of brain activity are the gold standard of computational neuroscience, and are critical for the long-term advancement of neuroscience and medicine. The research program in my lab reflects a tight integration of three distinct approaches: neuroscience experiments involving both classical electrophysiology and functional neuroimaging (fMRI); statistical analysis using methods adapted from nonlinear system identification and nonlinear regression; and theoretical modeling. Much of our research uses modern statistical tool