Jack Gallant

Jack Gallant is a cognitive neuroscientist whose research investigates how the brain represents and processes naturalistic information. He is best known for developing computational models that decode brain activity to reveal how sensory and cognitive information is encoded in the cerbral cortex. Gallant’s research integrates neuroimaging, computational neuroscience, and machine learning to map brain function during perception, language processing, and cognition. His work has advanced understanding of the neural basis of cognition and inspired new methods in brain-computer interfaces and AI.

He is Chancellor’s Professor of Psychology and Neuroscience at UC Berkeley, and co-director of the Henry H. Wheeler Brain Imaging Center. His research has been published in Nature, Science, and Neuron, and it is featured routinely in popular newspapers and magazines. At Berkeley, he teaches systems neuroscience and computational modeling, mentoring graduate students in the areas of neuroimaging, computational modeling of the brain, brain decoding and cognitive neuroscience.