Vipin Kumar delivers the eighth annual Borchert Lecture as part of the UMN Spatial Forum
Dr. Vipin Kumar, William Norris Professor and Head of the Computer Science and Engineering Department at the University of Minnesota, gave the eighth annual Borchert Lecture, which honors the late John Borchert, University of Minnesota Regents Professor in Geography and member of the U.S. National Academy of Science. David Borchert, one of Dr. Borchert's sons, attended the event named in honor of his father. This annual lecture features notable speakers in the area of geographic information science and this year was part of the campus-wide Spatial Forum and GIS Day celebration. Dr. Kumar's current research interests include data mining, high-performance computing, and their applications in Climate/Ecosystems and Biomedical domains. He is the Lead PI of a 5-year, $10 Million project, "Understanding Climate Change - A Data Driven Approach", funded by the NSF's Expeditions in Computing program that is aimed at pushing the boundaries of computer science research. He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used text book "Introduction to Parallel Computing", and "Introduction to Data Mining" both published by Addison-Wesley. Dr. Kumar's presentation, Understanding Global Change: Opportunities and Challenges for Data Driven Research, was well-attended and many excellent questions were asked by the audience. The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These information-rich datasets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth's ecosystem and for advancing the science of global change. This talk highlighted some of the challenges in analyzing such data sets and reported on early research results.