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.

天然人类世界的特征是高度相互联系的系统，其中一门学科没有能够确定全身风险和缓解目标的更广泛迹象。例如，气候变化和变化增加了农业，生态，能源，金融和水文学的风险？例如，在空间天气中，风险如何与能源，水和金融联系在一起？计算和数据科学的最新进展以及这些领域的每个域中的数据革命现在提供了一种解决这些问题的手段。研究人员共同建立了棱镜合作研究所，以开创跨不同领域的大规模，多分辨率，动态数据的整合，以改善风险的预测（极端结果和系统失败的潜在）。研究人员的愿景是开发一个跨域框架，该框架在领域专业知识的背景下利用大数据来发现新的关键风险指标，从整体上确定其互连，预测未来的风险和溢出潜力，并广泛衡量系统性风险。调查人员将与利益相关者合作，最终创建早期警告和目标，以减轻严重的风险，并为全球毁灭性事件做好准备；建立广泛而独特的伙伴关系，通过博士后研究人员和学生交流，研究务虚会和讲习班来教育下一代数据科学家；并通过招募和培训STEM中代表性不足的人（包括女性和代表性不足的少数族裔学生，以及通过Prism Data Library Web Services启用的方法，工具和数据集对利益相关者社区的影响）来扩大参与。研究人员将CRIS定义为可量化的信息，这些信息与累积或急性风险暴露于灾难性的，灾难性的损失或灾难性（累积）活性或灾难性事件有关。 Prism旨在确定许多领域中的关键风险和现有指标，并通过利用数据革命来发展新的CRIS； （ii）动态风险互连；研究人员将使用最先进的方法来适应诸如非机构，时空和多分辨率的属性等CRI复杂性，以动态模型和预测CRIS和PRISM旨在牢固地识别关键社会风险的稀疏铅滞后风险依赖性结构； （iii）系统性风险指标（SRIS）； Prism将通过预测关键风险溢出并通过创建SRI来促进利益相关者干预分析来对跨域系统风险进行建模； （iv）验证和利益相关者参与；研究人员将在综合案例研究上部署Prism分析框架，并具有明显的风险暴露（急性与累积）和灾难特征（直接与持续），并将征求关键利益相关者的定期投入有关关键风险及其决策变量的定期投入，以便更好地为政策实践提供了对国家科学的发展的一部分。 NSF数学和物理科学局内的HDR和数学科学划分。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

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

A Tuning-free Robust and Efficient Approach to High-dimensional Regression

• DOI: 10.1080/01621459.2020.1840989
• 发表时间: 2020-10
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Lan Wang;Bo Peng;Jelena Bradic;Runze Li;Y. Wu

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