Posters

Machine Learning (ML) is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed." As tactical and strategic planning timelines compress and increasingly large nonlinear datasets are acquired, autonomy and machine intelligence has to play a more critical role in the interpretation of data from planetary exploration missions and laboratory measurements. There is a need for capable systems that can rapidly and intelligently extract information from these datasets in a manner useful for scientific analysis. The community is starting to respond to this need by applying machine learning and deep learning approaches on various levels. This session will explore research that leverages machine learning methods to enhance our scientific understanding of planetary data, from astronomical observations, planetary exploration missions, as well as numerical simulations. Science objectives as diverse as image recognition, atmospheric retrieval, analysis of observed time series and of numerical simulation addressed through a variety of machine and deep learning tools will be considered.