AF: Small: Towards better geometric algorithms: Summarizing, partitioning and shrinking data

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New (and not so new) technologies like GPS, LIDAR, smart-phones and apps are now used to collect vast amounts of geometric and geographic data. In many such cases the data is too large to be handled by a single computer, and it needs to be broken up into small chunks, so that it can be handled by a cluster/cloud of computers. This award funds research to develop efficient algorithms for manipulating and summarizing such data. In particular, this award concentrates on the issue of how to partition such data efficiently in a balanced way, so that it can be efficiently processed.The algorithms and insights obtained from the technical work will benefit Computer Science and related disciplines where geometric data and algorithms are widely used. The PI hopes to broaden the scope of Computer Science (and Computational Geometry) by introducing new techniques, that would lead to faster and better algorithms, and potentially new applications of geometric data. The project will support and train at least two new PhD students in Computer Science at UIUC. The PI is committed to popularizing ideas and techniques that would be investigated by this proposal, by giving courses, publishing the research, potentially writing a new book on the topic, and use less convectional new tools to disseminate the research using blogs, social media, and online videos.