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Mark Cheung: "Heliophysics: Data, Science."

Abstract: The convergence of three trends, namely (1) open access scientific big data, (2) open source data analytics, and (3) machine learning (ML) and artificial intelligence (AI) promises to accelerate the rate of scientific discovery and the life cycle of operations to research / research to operations (O2R / R2O) in the Heliophysics discipline. This talk will showcase some recent applications of ML/AI to Heliophysics problems, including (a) prediction of the geomagnetic field variability in response to the solar wind, (b) spectropolarimetry for measuring the magnetic field on the solar surface, and (c) using deep learning to perform compressed sensing on extreme UV images of the solar corona. Some of the work in this presentation was made possible by NASA's Frontier Development Lab, a public-private partnership between the agency and industry partners (including the SETI Institute, IBM, Nvidia, Intel, kx & Lockheed Martin) whose mission is to use artificial intelligence to tackle problems related to planetary defense.

Bio: Mark Cheung is a Staff Physicist at Lockheed Martin Solar & Astrophysics Lab and a Visiting Scholar at Stanford University. He is Principal Investigator for the Atmospheric Imaging Assembly (AIA) instrument onboard NASA’s Solar Dynamics Observatory (SDO) and a Co-Investigator for NASA’s Interface Region Imaging Spectrograph (IRIS) mission. In addition, as Principal Investigator for one of NASA's Heliophysics Grand Challenges Research grants, he leads a team tasked to develop massively parallel numerical models to simulate solar flares and eruptions. He served as Heliophysics Mentor for NASA's Frontier Development Lab in 2017.

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