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Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to dif… Read more
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Prof. Valerio Carruba is currently an Associate Professor at the São Paulo State University (UNESP) in Brazil. He is one of the founders of the Machine Learning applied to Small Bodies (MASB) research group. He has published more than 70 papers, 12 of which are on applications of machine learning to dynamics of small bodies. His recent interests involve the use of deep learning for the identification of asteroids in secular resonant configurations and machine learning applied for asteroid families identification. Asteroid 10741 has been named Valeriocarruba by the International Astronomical Union. His recent paper Optimization of artificial neural networks models applied to the identification of images of asteroids’ resonant arguments recently won the CELMEC prize for "Innovative computational methods in Dynamical Astronomy".
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Dr. Evgeny Smirnov works in the field of the dynamics of asteroids. In 2017, he introduced a machine-learning approach based on the supervised learning to the identification procedure that decreases the computational time from weeks to seconds. The same year, he proposed a similar approach for asteroid families instead of the classical HCM method. Having a strong background in science and software development, Evgeny connects these areas and brings modern software development patterns and techniques in the field of astronomy.
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Dr. Dagmara Oszkiewicz is a Polish astronomer and planetary scientist. She received her PhD from the University of Helsinki, Finland in 2012 and is currently an assistant professor at Adam Mickiewicz University in Poznań, Poland. Her research focuses on physical and orbital properties of small Solar System bodies. Recently she expanded her research to include machine learning techniques to the analysis of asteroid spectro-photometric data. Her latest works include applications of machine learning algorithms to the classification of basaltic asteroids in the context of the formation of differentiated planetesimals (planetary embryos that existed in the Solar System 4 billion years ago) and comparison of various machine learning algorithms for the classification of spectro-photometric data from various large sky surveys.