Point Processes in Healthcare and Security Analytics: Nonparametric Estimation and Efficient Optimization

点过程模型在医疗保健和安全分析中的进步，例如在预测疾病发生、犯罪和网络安全攻击方面，有可能显著改善公共健康和安全。点过程是工程中一类重要但未得到充分重视的模型，它包含了时间动力学、信息扩散和循环行为。该项目将建立理论基础和计算方法，以便能够根据实时和大规模的交易数据对点过程进行有效的建模和估计。为了应对医疗保健和安全分析中新出现的数据丰富的挑战，该项目旨在打破当前理论和实践在点流程中的建模和计算限制，为这些重大的工程挑战提供一套强大的新工具。PIS致力于促进下一代工程师和数据科学家的教育和培训，特别是女性和代表性不足的少数群体。为点过程模型开发有效的推理分析和决策远远不能达到与高斯模型相同的成熟水平。在这个项目中，PI将利用许多领域的元素-优化、机器学习、非参数统计和信息论-来解决多变量Hawkes过程中的几个基本建模和计算障碍。该项目有四个主要研究方向：(I)研究非参数模型，以显著提高点过程捕获大规模和复杂事件数据的能力；(Ii)为点过程中的统计推理开发新的理论上有理论基础和实践上有效的学习和优化算法；(Iii)探索特定领域的结构，以实现似然和正则化之间的最佳权衡；(Iv)开发在线优化方案和工具，以促进对流事件数据的实时预测和推理。该项目开发的技术将用于推进特定医疗保健和安全相关应用的建模和预测，如发现疾病关系、跟踪药物不良反应以及检测犯罪和恐怖活动。该奖项反映了NSF的法定使命，并已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。

The advancement of point process models in healthcare and security analytics, such as in predicting disease occurrences, crime and cyber security attacks, has the potential to significantly improve public health and safety. Point processes are an important but underappreciated class of models in engineering that incorporate temporal dynamics, information diffusion, and recurrent behavior. This project will establish theoretical foundations and computational methods that enable efficient modeling and estimation of point processes from real-time and large-scale transactional data. To address emerging data-rich challenges in healthcare and security analytics, this project aims to break the modeling and computational limitations of current theory and practice in point processes, providing a powerful new set of tools for these significant engineering challenges. The PIs are committed to devoting their efforts to facilitate the education and training for next-generation engineers and data scientists, especially women and underrepresented minorities.The development of efficient inferential analysis and decision-making for point process models is far from reaching the same level of maturity as that of Gaussian models. In this project, the PIs will leverage elements from many fields - optimization, machine learning, nonparametric statistics, and information theory, to address several fundamental modeling and computational hurdles in the context of multivariate Hawkes processes. The project has four main research thrusts: (i) investigation of nonparametric models to significantly advance the capability of point processes in capturing large-scale and complex event data; (ii) development of novel theoretically grounded and practically efficient learning and optimization algorithms for statistical inference in point processes; (iii) exploration of domain-specific structure to achieve the optimal trade-off between likelihoods and regularizations; (iv) development of online optimization schemes and tools to facilitate real-time prediction and inference for streaming event data. The techniques developed in this project will be used to advance the modeling and prediction of specific healthcare and security related applications, such as discovery of disease relationships, tracking of adverse drug reactions, and detection of criminal and terrorist activities.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.