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: 1937978
• 负责人: Edoardo Airoldi
• 金额: $ 10.49万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937978&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.

许多现代数据收集，收集的目的是提供洞察国家利益的问题，如医疗和技术创新，通常测量快速发展的相互作用，除了传统的单位级测量，在网络的背景下。该项目开发了一个综合的研究和教育计划，使科学和定量分析的相互作用和其他组合测量，因为它们随着时间的推移而变化。正在解决的技术问题包括，但不限于：一个有效的表示，促进大规模网络的定量分析;模型的信息和行为如何随着时间的推移而演变的结果，他们嵌入的网络上下文;和快速算法来执行这些模型中的关键参数的估计。这些方法将在案例研究中得到证明，这些案例研究探讨：医疗创新在医生中的传播及其对健康的影响;美国的技术创新动态和非竞争协议的作用;网络上点对点通信的估计，来自被动监控的聚合流量。交互和其他组合测量的存在是数据中观察到的变化的来源创造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。网络过程的分析往往会引入一些约束，使得推理问题不成立，因为它们涉及大量的未知量来描述很少的观测。估计可能需要从极其受限的参数空间中采样并在其上积分。重要的是，相互作用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性思维;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元级观测之间的依赖性的一种手段。该项目通过统计和机器学习方法应对技术挑战。预期的技术成果包括但不限于：（1）多变量和动态网络的新小波分解;（2）给定网络上信息扩散的统计模型，以及连续时间内的非均匀网络动态模型;（3）这些模型的可扩展估计算法;以及（4）大数据推理的理论基础。这项研究将在哈佛与工业合作伙伴合作进行定性和定量评估。拟议的研究与跨学科教育计划相结合，这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点。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。该教育计划的关键要素包括开发统计机器学习课程;人人都可以在YouTube上观看讲座;在国家和国际会议和研讨会上提供教程;以及一本专著。外联活动包括为广大社区提供开源软件和网络工具，以及与工业伙伴合作，利用新的计算工具和算法，使其全球用户群受益。有关该项目的更多详细信息，请访问：http://www.fas.harvard.edu/~airoldi/career.html。