Insights Obtained From Mining Ship-tracks within NASA Data
Our team combed through half petabyte of satellite data to find rare features called ship-tracks. They can be used to tackle one of the key uncertainty of climate science. In this talk, we document how we use deep learning to achieve this with a small budget and present what we learned.
Speaker
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Dr. Tianle Yuan
Dr. Tianle Yuan is an associate research scientist at NASA GSFC and GESTAR-II UMBC. He specializes in applying deep learning and machine learning to remote sensing data and climate science. He has led several NASA projects in developing deep learning models to obtain high-level insights from data pools. He has a BS from Peking University and a PhD from Univ. Of Maryland, College Park.
DAX 2022
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DAX 2022 was was held on June 4, 2022 at UMBC and focused on data science, analytics, and general data exploration. More information can be found at https://daxconf.org
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