RTG: Understanding dynamic big data with complex structure

RTG：理解结构复杂的动态大数据

• 批准号: 1646108
• 负责人: Elizaveta Levina
• 金额: $ 250万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646108&HistoricalAwards=false
• 关键词: RTG; Understanding; dynamic; big; data

The emerging era of big data has brought with it new unique challenges in both research and training in Statistics. For the new types of statistical problems researchers now aim to solve, the size of available data has grown immensely in many cases, and the nature of the data has changed no less dramatically. Statisticians now work routinely with data that combine many different kinds of observations, from genetic data to brain images to smartphone data. This creates a need for new training approaches and their close integration with current research directions, so that PhD students and postdocs are prepared to take on new challenges as they become independent researchers. It also creates an opportunity for recruiting undergraduates into the field, increasing and diversifying the domestic STEM workforce. This project will train undergraduate and graduate students and postdocs in modern techniques for dynamic big data with complex structures, in modern teaching methods for statistics, and provide mentoring on all aspects of professional development. This project brings together three interlinked research streams: (1) statistical network analysis, (2) inference for dynamic systems, and (3) sequential decision making. This project will contribute to each of these areas, developing (1) realistic models for network community detection, link prediction and dynamically evolving networks, and tools for utilizing network connections to improve prediction of outcomes of interest on network-linked data; (2) practical algorithms with provably good properties for fitting complex partially observed Markov process models, with an emphasis on scalability; (3) sequential decision making algorithms based on reinforcement learning, with the goal of achieving excellent prediction performance and discovering interpretable decision variables. Each research stream will offer a short intensive graduate course and a regular interdisciplinary student workshop. Equally importantly, the streams will collaborate on topics that cut across these areas, such as inference for dynamically evolving networks or the role of social connections in predicting behavior and their impact on sequential decision making. Training undergraduates, PhD students, and postdocs in topics at the cutting edge of modern statistics will contribute to supplying much-needed statisticians and data scientists to both academia and industry, increasing and diversifying the STEM workforce. All three research streams have broad applications to areas beyond Statistics, such as neuroimaging, infectious disease transmission, and mobile health interventions. The project is thus expected to have wide-ranging impact on how the problems statisticians study are approached by domain scientists.

新兴的大数据时代给统计研究和培训带来了新的独特挑战。对于研究人员现在致力于解决的新型统计问题，在许多情况下，可用数据的大小已经大大增加，数据的性质也发生了巨大的变化。统计学家现在经常使用结合了联合收割机许多不同类型观察的数据，从基因数据到大脑图像再到智能手机数据。这就需要新的培训方法及其与当前研究方向的紧密结合，以便博士生和博士后在成为独立研究人员时准备迎接新的挑战。它还为招聘本科生进入该领域创造了机会，增加了国内STEM劳动力并使其多样化。该项目将培训本科生、研究生和博士后掌握复杂结构动态大数据的现代技术，掌握统计学的现代教学方法，并提供专业发展各方面的指导。 该项目汇集了三个相互关联的研究流：（1）统计网络分析，（2）动态系统的推理，（3）顺序决策。 该项目将对上述每个领域做出贡献，开发（1）用于网络社区检测、链接预测和动态演进网络的现实模型，以及用于利用网络连接来改进对网络链接数据上感兴趣的结果的预测的工具;（2）具有可证明的良好特性的实用算法，用于拟合复杂的部分观测马尔可夫过程模型，重点是可扩展性; (3)基于强化学习的顺序决策算法，目标是实现卓越的预测性能并发现可解释的决策变量。 每个研究流将提供一个短期的密集研究生课程和定期跨学科的学生研讨会。 同样重要的是，这些流将在跨越这些领域的主题上进行合作，例如动态演化网络的推理或社会联系在预测行为中的作用及其对顺序决策的影响。 在现代统计学的前沿领域对本科生、博士生和博士后进行培训，将有助于为学术界和工业界提供急需的统计学家和数据科学家，增加STEM劳动力并使其多样化。 所有这三个研究流都有广泛的应用领域超越统计，如神经成像，传染病传播，和移动的健康干预。 因此，该项目预计将产生广泛的影响，统计学家研究的问题是如何处理域科学家。

项目成果

Precise unbiased estimation in randomized experiments using auxiliary observational data

使用辅助观测数据在随机实验中进行精确无偏估计

• DOI: 10.1515/jci-2022-0011
• 发表时间: 2023
• 期刊: Journal of Causal Inference
• 影响因子: 1.4
• 作者: Gagnon-Bartsch, Johann A.;Sales, Adam C.;Wu, Edward;Botelho, Anthony F.;Erickson, John A.;Miratrix, Luke W.;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.

Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

邻接和拉普拉斯谱嵌入的样本外扩展的极限定理

• 发表时间: 2021
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Levin, K;Roosta, F;Tang, M.;Mahoney, M. W.;Priebe, C. E.
• 通讯作者: Priebe, C. E.

Social Media as an Alternative to Surveys of Opinions About the Economy

• DOI: 10.1177/0894439319875692
• 发表时间: 2019-09-26
• 期刊: SOCIAL SCIENCE COMPUTER REVIEW
• 影响因子: 4.1
• 作者: Conrad, Frederick G.;Gagnon-Bartsch, Johann A.;Hou, Elizabeth
• 通讯作者: Hou, Elizabeth

Altered resting-state functional connectivity in adolescents is associated with PTSD symptoms and trauma exposure

• DOI: 10.1016/j.nicl.2020.102215
• 发表时间: 2020-02
• 期刊: NeuroImage : Clinical
• 作者: Jony Sheynin;Jony Sheynin;E. Duval;Yana Lokshina;Yana Lokshina;J. C. Scott;Mike Angstadt;Daniel A Kessler;Li Zhang;Li Zhang;R. E. Gur;R. Gur;Israel Liberzon;Israel Liberzon

Online Multiclass Boosting with Bandit Feedback

带有 Bandit 反馈的在线多类提升

• 发表时间: 2018
• 作者: Daniel T. Zhang;Young Hun Jung;Ambuj Tewari
• 通讯作者: Ambuj Tewari