AF: Small: Efficient Proximity and Similarity Search in Computational Geometry

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This award funds research to develop faster and better algorithms for handling data, and specifically geometric data. With the massive amount of data in the world nowadays, standard algorithms fall short. The PI and his students investigate ways to improve geometric search for nearest neighbor, clustering, data-compression, and similarity search between curves, especially for massive amount of data. Such algorithms are widely used in the real world to handle navigation tasks, pattern recognition, signature identification, etc.The algorithms and insights obtained from the technical work will benefit Computer Science and related disciplines where geometric 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. A complementary goal is also to introduce Computer Science techniques into other fields.The award will support and train two or more PhD students in Computer Science at UIUC. The PI is committed to popularizing ideas and techniques that will be investigated by giving courses, publishing the research, and using less convectional new tools to disseminate the research such as blogs, social media, and online videos.