ATD: Efficient and Effective Algorithms for Detection of Anomalies in High-dimensional Spatiotemporal Data with Large Amounts of Missing Data

利用具有混合类型多变量响应、稀疏和未知信号以及大量缺失值的高维真实数据来检测罕见异常事件是非常具有挑战性的。领域知识和数据与空间和时间变量的体系结构集成显著提高了模型的可靠性、可解释性和预测精度。为了预测异常，研究人员计划开发高效和有效的后验采样算法，并将这些方法应用于跨学科合作的各种真实数据。学生，包括那些来自代表不足的群体的学生，将参与以解决现实世界问题为特色的教育和外展活动。研究旨在开发新的异常检测算法。它们将利用来自领域知识(表示为先验分布)的混合信息和正则化约束来建立高效和有效的搜索方向。嵌入在似然函数和回归模型中的数据信息将减少参数估计的搜索空间。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Detecting rare events of anomalies using high-dimensional real-world data with mixed-type multivariate response, sparse and unknown signals and a large number of missing values is very challenging. The architectural integration of domain knowledge and data with spatial and temporal variables significantly improves the model reliability, interpretability, and prediction accuracy. For predicting anomalies, the investigator plans to develop efficient and effective posterior sampling algorithms and apply these methods to various real-word data with interdisciplinary collaborations. Students, including those from underrepresented groups, will be involved in education and outreach activities featuring real world problem-solving.The research aims to develop novel anomaly detection algorithms. These will leverage hybrid information from the domain knowledge (represented as prior distributions) and regularization constraints to establish an efficient and effective search direction. Data information embedded in likelihood functions and regression models will reduce the search space for parameter estimation.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.