Supporting U.S.-Based Students to Attend the 2012 IEEE International Conference on Data Mining (ICDM 2012)

支持在美国的学生参加2011年IEEE数据挖掘国际会议(ICDM 2012)PI：Ding，Wee，马萨诸塞大学波士顿分校该项目为NSF学生提供旅行奖学金，帮助注册数据挖掘或密切相关领域的博士学位的研究生出席并参加2012年12月10日至13日在比利时布鲁塞尔举行的2012年IEEE数据挖掘国际会议。将特别努力吸引妇女和在数据挖掘研究界任职人数不足的群体成员。NSF学生旅行奖获得者将能够参加全体会议并贡献演讲、研讨会和教程，这些都是SDM 2012的一部分，以及一个博士论坛。博士论坛旨在允许处于研究生涯早期阶段的博士生展示他们的工作，与其他大学的同行互动，以及接受领先数据挖掘研究人员的一对一指导。智力上的功绩。参加首屈一指的研究会议是博士生数据挖掘研究型高级培训的重要组成部分。ICDM 2012博士生论坛为博士生提供了一个机会，从数据挖掘领域的知名研究人员那里获得建设性的反馈和指导。这种互动有助于拓宽学生的培训范围，提高学生在本国机构进行的论文研究的质量，并最终帮助他们做出明智的决定，以推进他们的长期职业目标。更广泛的影响。数据挖掘在许多新兴的数据丰富的应用中发挥着越来越重要的作用，从计算生物学到计算机辅助发现和反恐努力。该奖项将有助于扩大妇女和数据挖掘研究社区中代表性不足的少数群体的成员的代表性。它还有助于培训配备了先进工具的新一代科学家队伍，以便从我们日益数字化和相互联系的世界中日益重要的领域的数据中提取知识。

Supporting U.S.-Based Students to Attend the 2011 IEEE International Conference on Data Mining (ICDM 2012)PI: Ding, Wei, University of Massachusetts Boston The project provides NSF Student Travel Fellowships to graduate students enrolled in Ph.D. programs in data mining or closely related areas to attend and participate in the 2012 IEEE International Conference on Data Mining in Brussels, Belgium, from December 10-13, 2012. Special efforts will be made to attract women and members of groups that are underrepresented within the data mining research community. The NSF student travel award recipients will be able to attend the plenary and contributed talks, workshops and tutorials that are part of SDM 2012, as well as a Doctoral Forum. The Doctoral Forum is designed to allow doctoral students who are at an early stage in their research careers to present their work, interact with their peers from other universities as well as receive one-on-one mentoring from leading data mining researchers. Intellectual Merit. Participation in premier research conferences is an essential component of research-based advanced training of Ph.D. students in data mining. The ICDM 2012 Doctoral Student Forum provides an opportunity for Ph.D. candidates to receive constructive feedback and mentoring from established researchers in data mining. Such interactions serve to broaden the training of students, enhance the quality of the dissertation research performed by the students at their home institutions, and ultimately, help them make informed decisions that advance their long-term career objectives. Broader Impact. Data mining is playing an increasingly important role in many emerging data-rich applications ranging from computational biology to computer-assisted discovery and counterterrorism efforts. The award will help broaden the representation of women and members of underrepresented minority groups within the Data Mining research community. It also contributes to the training of a cadre of new generation scientists equipped with sophisticated tools to extract knowledge from data in areas that are growing in importance in our increasingly digital, interconnected world.