Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis

合作研究：用于多层动态互连分析的预测风险调查系统（PRISM）

• 批准号: 2023755
• 负责人: Lan Wang
• 金额: $ 25.46万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-20 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023755&HistoricalAwards=false
• 关键词: Collaborative; Research; Predictive; Risk; Investigation

The natural-human world is characterized by highly interconnected systems, in which a single discipline is not equipped to identify broader signs of systemic risk and mitigation targets. For example, what risks in agriculture, ecology, energy, finance and hydrology are heightened by climate variability and change? How might risks in, for example, space weather, be connected with energy, water and finance? Recent advances in computing and data science, and the data revolution in each of these domains have now provided a means to address these questions. The investigators jointly establish the PRISM Cooperative Institute for pioneering the integration of large-scale, multi-resolution, dynamic data across different domains to improve the prediction of risks (potentials for extreme outcomes and system failures). The investigators' vision is to develop a trans-domain framework that harnesses big data in the context of domain expertise to discover new critical risk indicators, holistically identify their interconnections, predict future risks and spillover potential, and to measure systemic risk broadly. The investigators will work with stakeholders to ultimately create early warnings and targets for critical risk mitigation and grow preparedness for devastating events worldwide; form wide and unique partnerships to educate the next generation of data scientists through postdoctoral researcher and student exchanges, research retreats, and workshops; and broaden participation through recruiting and training of those under-represented in STEM, including women and underrepresented minority students, and impact on stakeholder communities via methods, tools and datasets enabled by PRISM Data Library web services.The PRISM Cooperative Institute's data-intensive cross-disciplinary research directions include: (i) Critical Risk Indicators (CRIs); The investigators define CRIs as quantifiable information specifically associated with cumulative or acute risk exposure to devastating, ruinous losses resulting from a disastrous (cumulative) activity or a catastrophic event. PRISM aims to identify critical risks and existing indicators in many domains, and develop new CRIs by harnessing the data revolution; (ii) Dynamic Risk Interconnections; The investigators will dynamically model and forecast CRIs and PRISM aims to robustly identify a sparse, interpretable lead-lag risk dependence structure of critical societal risks, using state-of-the-art methods to accommodate CRI complexities such as nonstationary, spatiotemporal, and multi-resolution attributes; (iii) Systemic Risk Indicators (SRIs); PRISM will model trans-domain systemic risk, by forecasting critical risk spillovers and via the creation of SRIs for facilitating stakeholder intervention analysis; (iv) Validation & Stakeholder Engagement; The investigators will deploy the PRISM analytical framework on integrative case studies with distinct risk exposure (acute versus cumulative) and catastrophe characteristics (immediate versus sustained), and will solicit regular input from key stakeholders regarding critical risks and their decision variables, to better inform their operational understanding of policy versus practice.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Mathematical Sciences within the NSF Directorate of Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自然-人类世界的特点是高度相互关联的系统，其中单一学科无法识别更广泛的系统性风险迹象和缓解目标。例如，气候变率和变化加剧了农业、生态、能源、金融和水文方面的哪些风险？太空天气等风险如何与能源、水和金融联系起来？计算和数据科学的最新进展，以及这些领域的数据革命，现在为解决这些问题提供了一种方法。双方共同建立了PRISM合作研究所，率先将不同领域的大规模、多分辨率、动态数据进行整合，以提高对风险（极端结果和系统故障的可能性）的预测。研究人员的愿景是开发一个跨领域框架，利用领域专业知识背景下的大数据来发现新的关键风险指标，全面识别它们的相互联系，预测未来风险和溢出潜力，并广泛衡量系统风险。调查人员将与利益攸关方合作，最终制定预警和减少重大风险的目标，并加强对全球破坏性事件的准备；建立广泛而独特的合作伙伴关系，通过博士后研究员和学生交流、研究务虚会和研讨会来培养下一代数据科学家；通过招募和培训STEM中代表性不足的群体，包括女性和代表性不足的少数族裔学生，扩大参与范围，并通过PRISM数据库网络服务提供的方法、工具和数据集影响利益相关者社区。PRISM合作研究所的数据密集型跨学科研究方向包括：(i)关键风险指标（CRIs）；研究人员将cri定义为可量化的信息，这些信息与由灾难性（累积）活动或灾难性事件导致的破坏性、毁灭性损失的累积或急性风险暴露特别相关。PRISM旨在识别许多领域的关键风险和现有指标，并通过利用数据革命制定新的危机评估指标；动态风险相互联系；研究人员将动态建模和预测CRI， PRISM旨在使用最先进的方法来适应CRI的复杂性，如非平稳、时空和多分辨率属性，稳健地识别关键社会风险的稀疏、可解释的领先-滞后风险依赖结构；系统风险指标（SRIs）；PRISM将对跨领域的系统性风险进行建模，方法是预测关键风险溢出，并通过创建sri促进利益相关者干预分析；（iv）验证和利益相关者参与；调查人员将在具有不同风险暴露（急性与累积）和灾难特征（即时与持续）的综合案例研究中部署PRISM分析框架，并将定期征求关键利益相关者关于关键风险及其决策变量的意见，以更好地告知他们对政策与实践的操作理解。该项目是美国国家科学基金会“利用数据革命（HDR）大创意”活动的一部分，由HDR和美国国家科学基金会数学与物理科学理事会数学科学部共同支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Analysis of animal-related electric outages using species distribution models and community science data

使用物种分布模型和社区科学数据分析与动物相关的停电

• DOI: 10.1088/2752-664x/ac7eb5
• 发表时间: 2022
• 期刊: Environmental Research: Ecology
• 作者: Feng, Mei-Ling E;Owolabi, Olukunle O;Schafer, Toryn L;Sengupta, Sanhita;Wang, Lan;Matteson, David S;Che-Castaldo, Judy P;Sunter, Deborah A
• 通讯作者: Sunter, Deborah A

Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

对“一种无需调整的稳健且高效的高维回归方法”的反驳

• DOI: 10.1080/01621459.2020.1843865
• 发表时间: 2020
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Wang, Lan;Peng, Bo;Bradic, Jelena;Li, Runze;Wu, Yunan
• 通讯作者: Wu, Yunan

Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes

• DOI: 10.1111/biom.13337
• 发表时间: 2019-11
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Y. Wu;Lan Wang

High‐dimensional quantile regression: Convolution smoothing and concave regularization

• DOI: 10.1111/rssb.12485
• 发表时间: 2021-09
• 期刊: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
• 作者: Kean Ming Tan;Lan Wang;Wen-Xin Zhou

A robust statistical analysis of the role of hydropower on the system electricity price and price volatility

• DOI: 10.1088/2515-7620/ac7b74
• 发表时间: 2022-03
• 期刊: Environmental Research Communications
• 影响因子: 2.9
• 作者: Olukunle O. Owolabi;K. Lawson;Sanhita Sengupta;Ying Huang;Lan Wang;Chaopeng Shen;Mila Getmansky Sherman;D. Sunter