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The goal of my lab is to understand the mechanisms underlying visual memory. Our approach is to develop computational models and algorithms that can explain, predict, and ultimately regulate the brain responses (in the form of neural population responses and the consequent behaviors) during visual tasks, especially ones that require short- and long-term memory. We use ideas from machine learning, neuroscience, and cognitive science to build these models and we validate them on electrophysiological, neuroimaging, and behavioral measurements from animal models. The main techniques used in the lab are computational modeling with neural networks, neuroimaging, and behavioral experiments.