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Peter Lawrence Capak is currently the Architect of Perception Systems at the Oculus division of Facebook. His current focus is developing machine perception technologies, sensors, displays, and compute architectures for the next generation of augmented (AR), mixed (MR) and virtual reality (VR) systems. His research has focused on using physical modeling and advanced statistical methods including artificial intelligence and machine learning to extract information from very large multi-wavelength (hyper-spectral) data sets. He has primarily used this to study structure formation in the universe, cosmology, and the nature of dark matter and dark energy.

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  • Peter Capak (en)
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  • Peter Lawrence Capak is currently the Architect of Perception Systems at the Oculus division of Facebook. His current focus is developing machine perception technologies, sensors, displays, and compute architectures for the next generation of augmented (AR), mixed (MR) and virtual reality (VR) systems. His research has focused on using physical modeling and advanced statistical methods including artificial intelligence and machine learning to extract information from very large multi-wavelength (hyper-spectral) data sets. He has primarily used this to study structure formation in the universe, cosmology, and the nature of dark matter and dark energy. (en)
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  • Peter Lawrence Capak (en)
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  • Peter Lawrence Capak (en)
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  • http://commons.wikimedia.org/wiki/Special:FilePath/Peter_Capak.jpg
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  • Astronomy, Machine Learning, Space Sciences (en)
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  • Cosmology, Structure Formation, Dark Matter, Dark Energy Galaxy Evolution (en)
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  • Peter Lawrence Capak is currently the Architect of Perception Systems at the Oculus division of Facebook. His current focus is developing machine perception technologies, sensors, displays, and compute architectures for the next generation of augmented (AR), mixed (MR) and virtual reality (VR) systems. His research has focused on using physical modeling and advanced statistical methods including artificial intelligence and machine learning to extract information from very large multi-wavelength (hyper-spectral) data sets. He has primarily used this to study structure formation in the universe, cosmology, and the nature of dark matter and dark energy. (en)
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