AF: Small: Applied Algorithims: Tech Transfer from the Algorithims Toolbox II

AF：小：应用算法：算法工具箱 II 的技术转移

• 批准号: 1117381
• 负责人: David Karger
• 金额: $ 40万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117381&HistoricalAwards=false
• 关键词: AF; Small; Applied; Algorithims; Tech

Over the past decades, the field of algorithms has developed a toolbox of theoretical techniques that let computer systems to do more with less---to solve harder problems more quickly, using less memory, less communication with other computers, and less of a human user's assistance. One measure of algorithms' research success has been the large number of implemented systems whose designs have been impacted by contributions from the algorithms community. There are many more such successes waiting in the wings for individuals or groups who can draw the connection between an existing algorithmic technique and an existing applied problem. Often, the biggest challenge is recognizing the connection between the practitioner's problem statement and the proper algorithmic solution techniques. This research addresses the process of ``technology transfer'' from the algorithmic toolbox to other computer science domains.The investigator is working closely with practitioners in various areas of computer science to identify computational problems whose efficient solution would advance their research agendas, dig through the theory toolbox to find techniques that, properly adapted, can be used to efficiently solve those problems, and assist in such adaptation. Domains being addressed include natural language processing, detection of influence pathways in biological networks, traffic route planning that accounts for uncertain delays, network coding for efficient use of communication bandwidth, and efficient use of crowdsourced computation. But rather than being driven by a particular problem domain, the investigator is interested in the overall process for applying theoretical work in algorithms to problems in the practical domain, and is always seeking new applied problems that can benefit from this approach.Successful completion of the proposed work will contribute advancement to many different branches of computer science. The contributions to other branches of computer science will, in turn, allow them to achieve their goals of broad impact on society. The investigator also hopes to increase the general sense of connection between theoreticians and practitioners, yielding increased collaborations and successful applications of algorithms to theory beyond those made directly during this project. The project will contribute to research training by continuing to employ large numbers of students with attention given to gender diversity.

在过去的几十年里，算法领域已经发展出一个理论技术工具箱，让计算机系统用更少的资源做更多的事情——更快地解决更难的问题，使用更少的内存，减少与其他计算机的通信，减少人类用户的帮助。算法研究成功的一个衡量标准是大量的实现系统，其设计受到算法社区贡献的影响。还有更多这样的成功在等待着那些能够在现有算法技术和现有应用问题之间建立联系的个人或团体。通常，最大的挑战是认识到实践者的问题陈述和适当的算法解决技术之间的联系。这项研究解决了从算法工具箱到其他计算机科学领域的“技术转移”过程。研究者正在与计算机科学各个领域的实践者密切合作，以确定计算问题的有效解决方案将推进他们的研究议程，挖掘理论工具箱，找到适当适应的技术，可以用来有效地解决这些问题，并协助这种适应。涉及的领域包括自然语言处理、生物网络中影响路径的检测、考虑不确定延迟的交通路线规划、有效利用通信带宽的网络编码以及有效利用众包计算。但是，研究者对将算法中的理论工作应用于实际领域的问题的整个过程感兴趣，而不是被特定的问题领域所驱动，并且总是在寻找可以从这种方法中受益的新的应用问题。这项工作的成功完成将促进计算机科学的许多不同分支的发展。反过来，对计算机科学其他分支的贡献将使他们能够实现对社会产生广泛影响的目标。研究者还希望增加理论家和实践者之间的联系，产生更多的合作，并成功地将算法应用于理论，而不仅仅是在这个项目中直接做出的。该项目将继续雇用大量学生，并注意性别多样性，从而促进研究训练。