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

该奖项资助研究开发更快、更好的数据处理算法，特别是几何数据。随着当今世界海量数据的出现，标准的算法已经不能满足需求。PI和他的学生研究如何改进最近邻的几何搜索、聚类、数据压缩和曲线之间的相似性搜索，特别是对于海量数据。这些算法在现实世界中被广泛应用于导航任务、模式识别、签名识别等。从技术工作中获得的算法和见解将有助于计算机科学和相关学科广泛使用几何算法。PI希望通过引入新技术来扩大计算机科学(和计算几何)的范围，这将导致更快和更好的算法。另一个补充目标是将计算机科学技术引入其他领域。该奖项将支持和培训两名或更多UIUC的计算机科学博士生。PI致力于普及将通过开设课程、发布研究成果以及使用博客、社交媒体和在线视频等较少常规的新工具来传播研究成果的想法和技术。

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.