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Karsten Steinhaeuser is a Research Associate in the Department of Computer Science and Engineering at the University of Minnesota. His primary responsibilities currently include two major research efforts: an NSF Expeditions in Computing on Understanding Climate Change: A Data Driven Approach and the GOPHER project, which is an R&D partner in the Planetary Skin Institute.
His research interests are centered around data mining and machine learning, in particular the construction and analysis of complex networks, with applications in diverse domains including (but not limited to) climate, ecology, and social networks. He is actively involved in shaping an emerging research area called climate informatics, which lies at the intersection of computer science and climate sciences, and his interests are more generally in interdisciplinary research and scientific problems relating to climate change and sustainability. He co-organizes the IEEE ICDM Workshop on Knowledge Discovery from Climate Data and the International Workshop on Climate Informatics, among others, and is engaged in numerous other professional service and community building activities.
He earned his PhD in Computer Science and Engineering (co-advised by Prof. Nitesh Chawla and Prof. Auroop Ganguly) from the University of Notre Dame in May 2011, where he was a member of the Data, Inference, Analysis and Learning (DIAL) laboratory as well as the Interdisciplinary Center for Network Science and Applications (iCeNSA); he also spent three years of his PhD studies as a member of the Geographic Information Science and Technology Group at Oak Ridge National Laboratory. He previously received an MS in Computer Science and Engineering (2007) and a BS, Summa Cum Laude in Computer Science (2005), both from the University of Notre Dame.
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LARGE-SCALE MACHINE LEARNING IN THE EARTH SCIENCES (2017)
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user-607cde9d4c775e0497f57189(2016)
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Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (2014)
KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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August, 2014pp.1699-1708, (2014)
Managing and Mining Sensor Datapp.505-530, (2013)
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