CRII: RI: TRUST—TRustworthy Uncertainty Propagation for Sequential Time-Series Analysis

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。通过物联网产生的大量时间序列数据、医疗保健的数字化以及智慧城市的兴起，催生了对称职的时间序列建模、分析和预测的需求。许多关键应用都依赖于时间序列分析，包括视频分析、股票市场分析、地震预测、经济预测、医疗保健监测和疾病预测。最近的序列机器学习（ML）模型在处理整个数据序列（如语音或视频）和学习时间序列的长期依赖关系方面取得了显着的成功。然而，这些序列模型无法理解或评估它们的不确定性，特别是在异质和嘈杂环境或对手存在的情况下进行预测时。例如，由于心电图（ECG）生理信号中的人为因素而错过对心力衰竭的预测，或者未能检测到软件产品中的安全漏洞，可能会对人员和行业造成悲剧性的健康、财务和社会损害。该项目将为现代ML序列模型中的不确定性量化、自我评估和对抗检测提供重要的理论和算法基础，以实现安全可靠的时间序列智能机器。该项目将开发在噪声条件和对抗性影响下具有普遍鲁棒性的开创性算法。主要的焦点是序列ML算法与提供的解决方案的量化不确定性。提议算法的开源实现将公开提供，以便快速传播和贡献给ML社区。此外，拟议的研究将支持德克萨斯大学格兰德谷分校多样化的研究生和本科生的跨学科发展，并开发新的课程、证书、该项目的主要技术目标是倡导一种新的贝叶斯估计框架，该框架在非线性和非高斯系统中用于最优跟踪的强大统计框架的启发下，跨模型的非线性层传播分布。对模型在噪声条件和对抗性攻击下的性能和不确定性度量进行全面分析，将为在广泛的实际应用中部署顺序ML算法铺平道路。这项研究的应用包括工业系统安全领域的工业和医疗保健合作伙伴（与洛克希德马丁公司合作）和脑肿瘤检测和磁共振成像监测（与mrrimath， LLC合作）。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The massive production of time-series data through the internet of things, the digitalization of healthcare, and the rise of smart cities have surged the need for competent time-series modeling, analysis, and forecasting. Many critical applications rely on time-series analysis, including video analytics, stock market analysis, earthquake prediction, economic forecasting, healthcare monitoring and disease prognoses. Recent sequence machine learning (ML) models have achieved remarkable success in processing entire sequences of data (such as speech or video) and learning long-term dependencies of time-series. However, these sequence models are unable to understand or assess their uncertainty, particularly critical when predicting in heterogeneous and noisy environments or in the presence of an adversary. For example, missing a prediction of heart failure due to artifacts in the electrocardiogram (ECG) physiological signal or failing to detect a security vulnerability in a software product could cause tragic health, financial and societal damage to people and industries. This project will develop significant theoretical and algorithmic foundations for uncertainty quantification, self-assessment, and adversarial detection in modern ML sequence models towards safe and reliable time-series intelligent machines. The project will develop pioneering algorithms that are universally robust under noisy conditions and adversarial susceptions. The main focus is on sequential ML algorithms with quantified uncertainty for the provided solutions. Open-source implementations of the proposed algorithms will be publicly available for rapid dissemination and contribution to the ML community. Furthermore, the proposed research will support the cross-disciplinary development of a diverse cohort of graduate and undergraduate students at the University of Texas Rio Grande Valley and develop new courses, certificates, and research projects in trustworthy and robust ML.The primary technical aim of the project advocates a novel Bayesian estimation framework that propagates distributions across models’ non-linear layers inspired by powerful statistical frameworks for optimal tracking in non-linear and non-Gaussian systems. A comprehensive analysis of models’ performance and uncertainty measures under noisy conditions and adversarial attacks will pave the way for deploying sequential ML algorithms in a wide range of real-world applications. Applications of this research include industry and healthcare partners in the areas of security of industrial systems (in collaboration with Lockheed Martin Inc.) and brain tumor detection and surveillance from magnetic resonance imaging (in collaboration with MRIMath, LLC).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.