CAREER: Quantifying diffusion and dynamics on healthcare, innovation and communication networks

职业：量化医疗保健、创新和通信网络的扩散和动态

• 批准号: 1149662
• 负责人: Edoardo Airoldi
• 金额: $ 47万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149662&HistoricalAwards=false
• 关键词: CAREER; Quantifying; diffusion; dynamics; healthcare

Many modern data collections, gathered for the purpose of providing insights into matters of national interest such as medical and technological innovation, typically measure quickly evolving interactions, in addition to traditional unit-level measurements, in the context of a network. This project develops an integrated research and educational program to enable scientific and quantitative analyses of interactions and other combinatorial measurements as they change over time. Technical problems being addressed include, but are not limited to: an efficient representation that facilitates quantitative analyses of large-scale networks; models of how information and behavior evolve over time as a consequence of the network context they are embedded in; and fast algorithms to perform estimation of critical parameters in these models. These methods will be demonstrated on case studies exploring: the diffusion of medical innovations among physicians and its impact on health; technological innovation dynamics in the United States and the role of non-compete agreements; the estimation of point-to-point communications on a network, from aggregate traffic that is passively monitored.The presence of interactions and other combinatorial measurements as a source of observed variation in the data creates new statistical and inferential challenges. For instance, generalized linear model theory needs to be extended to responses on a network. The analysis of processes on a network often induces constraints that make the inferential problems ill posed, since they involve a large number of unknown quantities to describe few observations. Estimation may require sampling from, and integrating over, extremely constrained parameter spaces. Importantly, interactions do not necessarily encode statistical dependence. In this sense, dealing with observed interactions requires original thinking; the data settings they entail are not amenable to analysis with classical methods, in which interactions are inferred as a means to encode dependence among unit-level observations. This project tackles technical challenges with a statistical and machine learning approach. Anticipated technical results include, but are not limited to: (1) a new wavelet decomposition of multivariate and dynamic networks; (2) statistical models of diffusion of information on a given network, and models of inhomogeneous network dynamics in continuous time; (3) scalable estimation algorithms for these models; and (4) theoretical foundations of inference with big data. This research will be evaluated qualitatively and quantitatively, at Harvard and in collaboration with industrial partners.The proposed research is integrated with an interdisciplinary educational program, which will attract undergraduates to research at the intersection of statistics and computer science, in the context of problems of national importance. It will provide opportunities to actively encourage students from underrepresented groups to pursue careers in statistics and computer science. Key elements of the educational program include the development of a statistical machine learning curriculum; lectures on YouTube available to everyone; tutorials at national and international conferences and workshops; and a monograph. Outreach activities include open-source software and webtools for the community at-large, and a collaborative effort with industrial partners to leverage the new computational tools and algorithms for benefiting their pools of users worldwide. Additional details regarding the project can be found at: http://www.fas.harvard.edu/~airoldi/career.html.

为了深入了解医疗和技术创新等国家利益事项而收集的许多现代数据收集，除了传统的单位级测量外，通常还在网络背景下测量快速演变的相互作用。该项目开发了一个综合的研究和教育计划，使相互作用和其他组合测量随时间变化的科学和定量分析成为可能。正在处理的技术问题包括但不限于：促进大规模网络定量分析的有效表示；信息和行为如何随着时间的推移而演变的模型，这是它们所嵌入的网络环境的结果；以及在这些模型中执行关键参数估计的快速算法。这些方法将在案例研究中进行演示，探讨：医学创新在医生之间的传播及其对健康的影响；美国的技术创新动态和竞业禁止协议的作用；根据被动监控的总流量对网络上点对点通信的估计。相互作用和其他组合测量作为观测数据变化的来源，产生了新的统计和推理挑战。例如，广义线性模型理论需要扩展到网络上的响应。对网络过程的分析通常会引入约束，使推理问题不确定，因为它们涉及大量的未知量来描述很少的观测值。估计可能需要从极端受限的参数空间中采样并对其积分。重要的是，交互不一定编码统计依赖性。从这个意义上说，处理观察到的相互作用需要原创思维；它们所需要的数据设置不适合用经典方法进行分析，在经典方法中，相互作用被推断为编码单位级观测之间依赖关系的手段。该项目通过统计和机器学习方法解决技术挑战。预期的技术成果包括但不限于：(1)多元动态网络的新小波分解；(2)信息在给定网络上扩散的统计模型和连续时间的非齐次网络动力学模型；(3)模型的可扩展估计算法；(4)大数据推理的理论基础。这项研究将在哈佛大学进行定性和定量评估，并与工业伙伴合作。拟议的研究与一个跨学科的教育计划相结合，该计划将吸引本科生在国家重要问题的背景下研究统计学和计算机科学的交叉。它将提供机会，积极鼓励来自代表性不足群体的学生从事统计和计算机科学方面的职业。教育计划的关键要素包括开发统计机器学习课程；每个人都可以在YouTube上观看讲座；在国家和国际会议和讲习班上提供辅导；还有一本专著。拓展活动包括面向广大社区的开源软件和网络工具，以及与工业伙伴的协作努力，以利用新的计算工具和算法，使其全球用户池受益。有关该项目的更多细节可以在http://www.fas.harvard.edu/~airoldi/career.html上找到。