Supporting US-Based Students to Participate in the 2017 IEEE International Conference on Data Mining (ICDM 2017)

该奖项为参加2017年11月18日至21日在洛杉矶新奥尔良举行的2017年国际数据挖掘会议（ICDM 2017）的16名美国研究生提供旅费支持（http://www.icdm2017.bigke.org）。会议吸引了新的和原创的研究，以及来自美国和国外的一些顶级数据挖掘研究人员来讨论他们的最新成果。会议涵盖了与数据挖掘相关的许多主题的研究进展，包括统计学、机器学习、模式识别、数据库、数据仓库、数据可视化、基于知识的系统和高性能计算。ICDM会议的论文集将通过IEEE计算机学会分发，并将通过IEEE探索数字图书馆提供。除了强大的技术计划外，会议还包括关于数据挖掘的新兴主题的研讨会、教程、小组讨论、演示和博士论坛。博士论坛旨在为博士生提供一个互动的环境，在这个环境中，博士生可以与同行和数据挖掘社区的高级研究人员会面，交流他们的想法和经验。鉴于数据挖掘在数据科学新兴领域的重要性，ICDM组织者认识到在数据挖掘研究中招募、吸引和留住学生的重要性。参加会议使学生能够与同行和专家分享他们的原创研究成果，了解数据挖掘领域的新算法和工具，并获得应用数据挖掘解决各种跨学科问题的经验。美国学生的参与对发展美国未来科技劳动力的竞争力至关重要。

This award provides travel support for 16 U.S.-based graduate students to participate in the 2017 International Conference on Data Mining (ICDM 2017), held in New Orleans, LA, November 18th - 21st, 2017 (http://www.icdm2017.bigke.org). The conference attracts new and original research, and some of the top data mining researchers from the U.S., and abroad to discuss their latest. The conference covers advancement in research in many topics relevant to data mining that include statistics, machine learning, pattern recognition, databases, data warehouses, data visualization, knowledge-based systems and high-performance computing. The proceedings of the ICDM conference will be distributed through the IEEE Computer Society and will be available through the IEEE Explore Digital Library. Besides having a strong technical program, the conference features workshops in emerging topics on data mining, tutorials, panels, demos, and the PhD Forum. The PhD Forum is designed to provide an interactive environment in which PhD students can meet, exchange their ideas and experiences both with peers and with senior researchers from the data mining community. The ICDM organizers recognize the importance of recruiting, engaging and retaining students in data mining research given the importance of this topic in emerging field of data science. The conference participation enables the students to share their original research results with their peers and experts, learn about new algorithms and tools in the field of data mining, and obtain experience with application of data mining for various transdisciplinary problems. Participation of U.S.-based students is critical to developing U.S. competitiveness of future workforce in science and technology.