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Sean McGregor: "Interdisciplinary Collaboration: Finding new Ways of Extracting Scientific Insights from Solar Observations."

Abstract: Recent advances in machine learning algorithms provide a new toolset for extracting scientific insights from NASA data. Effectively leveraging these advances requires interdisciplinary collaboration between researchers that know the data and physical context (e.g., planetary scientists/astrophysicists/heliophysicists), and machine learning researchers than can bring outside expertise and new techniques.

One collaboration case study comes from NASA's Frontier Development Lab (FDL), a public-private partnership between the agency and industry partners (including the SETI Institute, IBM, Nvidia, Intel, kx, & Lockheed Martin). During an 8 week intensive research collaboration facilitated by the FDL, a team of 5 heliophysicists and machine learning researchers developed a neural network connecting solar UV images taken by SDO/AIA into forecasts of maximum x-ray emissions. In addition to potentially changing the practice of operational flare forecasting, the model has shown that it learns structure in the data that may inform new theoretically rigorous solar models and our understanding of flaring mechanisms.

Bio: Sean defended his Machine Learning PhD in June at Oregon State University. His research centered on sequential decision making (i.e., reinforcement learning) under the supervision of Thomas Dietterich. Specifically, Sean solved problems associated with bringing machine learning methods to public policy, including the optimization and visualization of forest wildfire suppression decisions. He is published in the academic literatures for work in machine learning, surrogate modeling, visual analytics, and human-computer interaction.

Continuing his penchant for interdisciplinary research, Sean spent the summer at NASA Ames to work with heliophysicists at the Frontier Development Lab (FDL). The team's 8 weeks of work resulted in a deep learning framework for heliophysics research, which shows promising results in the problem of solar flare forecasting.

Outside his own research, Sean is also the technical manager of the IBM Watson AI XPRIZE -- a multi-year $5 million prize for solving grand challenges with artificial intelligence.

Sean is originally from San Diego and enjoys wave and river sports. He is currently in a passive job search that will accelerate in October-- following the publication of a final dissertation chapter, the FDL work, and the completion of the first year of the AI XPRIZE.

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