EAGER: Models, analytics, and algorithms for data driven applications

EAGER：数据驱动应用程序的模型、分析和算法

• 批准号: 1523374
• 负责人: Zhengdao Wang
• 金额: $ 18万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1523374&HistoricalAwards=false
• 关键词: EAGER; Models; analytics; algorithms; data

Data driven applications are becoming increasingly prevalent as our ability to collect data continue to increase and the cost continues to decrease. Examples of such applications include social networks (twitter, facebook, etc.), bioinformatics (gene regulatory networks, genomic sequence analysis), and environmental monitoring using wireless sensor networks. As more databecome available, how to convert data to actionable information to guide decision is a pending problem that will have many applications. In addition to being "big", the data and underlying phenomena also exhibit time-variations which add to thedifficulty of gleaning useful information from data. To cope with these challenges, this project will develop an flexible algorithmic framework using Dynamic Bayesian Networks, which can sufficiently describe the physical reality, and at the same time are expressive enough to allow for machine based learning, optimization, inference and adaptation to dynamical data. Such a computational framework can assimilate data continuously, adaptively, and reduce the data to information ina format that is logical, intuitive, and amenable to human interactions.Such features are particularly needed for large-scale,data-intensive engineering systems and networks such as structural health monitoring, surveillance and disease discovery using gene regulatory networks. Intellectual Merit:The proposed project will build on existing work on graphical models and explore the largely open area of learning dynamical graphical models based on measured data. Algorithms will be developed based on convex optimization formulation for onlineadaptation of the graphical models. Partially known structural information, and issues with noisy, incomplete, corrupted, or missing data will be examined. The proposed research will advance the status of the art of learning from dynamical data,and explore the many under-explored connections between signal processing, and information theory, statistics,and machine learning.Broader Impact: The proposed research will develop generic models and analytical tools for data based applications. It will also generate low-complexity algorithms that can be adapted to a large number of applications such aswireless sensor networks, gene-regulatory networks, and genomic sequence analysis. The research will allow datathat are made available through digital technologies to be converted to information automatically by computers,which can then be understood and acted upon by humans. The educationalgoal of this proposed project is to efficientlintegrate research with educational activities and to train both undergraduate and graduate students in interdisciplinaryareas to produce next-generation engineers. Efforts will be made to invite women, underpresented, and minority undergraduatestudents to participate in the proposed research.

随着我们收集数据的能力不断提高，成本不断降低，数据驱动的应用程序变得越来越普遍。这样的应用的示例包括社交网络（twitter、facebook等），生物信息学（基因调控网络、基因组序列分析）和使用无线传感器网络的环境监测。随着越来越多的数据库的出现，如何将数据转换为可操作的信息来指导决策是一个有待解决的问题，将有许多应用。除了“大”之外，数据和潜在的现象还表现出时间变化，这增加了从数据中收集有用信息的难度。为了科普这些挑战，该项目将使用动态贝叶斯网络开发一个灵活的算法框架，该框架可以充分描述物理现实，同时具有足够的表达能力，以允许基于机器的学习，优化，推理和适应动态数据。这样的计算框架可以连续地、自适应地吸收数据，并将数据简化为逻辑、直观、易于人机交互的信息，这些特征对于大规模、数据密集型的工程系统和网络（如结构健康监测、监视和使用基因调控网络的疾病发现）来说是特别需要的。 智力优势：拟议的项目将建立在图形模型的现有工作，并探索基于测量数据学习动态图形模型的大部分开放领域。算法将开发基于凸优化配方的图形模型的onlineadaptation。将检查部分已知的结构信息以及噪声、不完整、损坏或缺失数据的问题。该研究将推动从动态数据中学习的艺术的发展，并探索信号处理、信息论、统计学和机器学习之间的许多未被探索的联系。更广泛的影响：该研究将为基于数据的应用开发通用模型和分析工具。它还将产生低复杂度的算法，可以适应大量的应用，如无线传感器网络，基因调控网络和基因组序列分析。这项研究将允许通过数字技术提供的数据被计算机自动转换为信息，然后人类可以理解并采取行动。该项目的教育目标是有效地将研究与教育活动结合起来，培养跨学科领域的本科生和研究生，以培养下一代工程师。将努力邀请女性、弱势群体和少数民族大学生参与拟议的研究。