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

GPS、LIDAR、智能手机和应用程序等新技术(并不是很新)现在被用来收集大量的几何和地理数据。在许多这样的情况下，数据太大，单台计算机无法处理，需要将其分解成小块，以便可以由计算机集群/云处理。该奖项资助研究开发处理和汇总此类数据的高效算法。这一奖项特别关注如何以平衡的方式有效地划分这些数据，以便能够高效地处理这些数据。从技术工作中获得的算法和见解将使计算机科学和广泛应用几何数据和算法的相关学科受益。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，并潜在地对几何数据进行新的应用。该项目将支持和培训至少两名加州大学计算机科学专业的新博士生。PI致力于普及这项提案将调查的想法和技术，通过开设课程、出版研究成果、可能就这一主题撰写一本新书，并使用较少常规的新工具通过博客、社交媒体和在线视频传播研究成果。

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.