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Jonathan Pillow is a Professor at the Princeton Neuroscience Institute, with affiliations in the Department of Psychology, the Center for Statistics and Machine Learning, and the Program in Applied and Computational Mathematics at Princeton University. He received a Ph.D. in neural science from New York University in 2005, where he worked under the supervision of Eero Simoncelli, and completed postdoctoral training at the Gatsby Computational Neuroscience Unit at University College London. Prior to joining Princeton, he served as an Assistant Professor at the University of Texas at Austin from 2009 to 2014.
Pillow’s research focuses on computational neuroscience, developing statistical and machine learning methods to analyze high-dimensional neural data, characterize neural encoding and decoding, and model sensory processing, decision-making, and latent structure in neural populations. His work includes collaborations with experimental groups on topics such as sensory-motor decision making, working memory, and scalable inference methods for electrophysiology and imaging data. He has received the Sloan Research Fellowship in 2011, the Presidential Early Career Award for Scientists and Engineers in 2012, and the Graduate Mentoring Award from Princeton University in 2019. Representative publications include papers in journals such as Neuron, Nature Neuroscience, Science, and the Journal of Machine Learning Research. Pillow’s contributions advance understanding of neural computations through rigorous statistical modeling.
Discover how researchers at CSHL, Princeton, and CMU compressed a massive AI model using macaque V4 neuron data into a pocket-sized powerhouse, revolutionizing neuroscience and AI.
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