ATD: Collaborative Research: Theory and Algorithms for Real-Time Threat Detection from Massive Data Streams

国家安全利益要求提高对各种对手的行动的认识。这种对信息的贪婪胃口导致了铺天盖地的时空数据。为了有效地管理这种数据洪流，需要新的数学；本研究项目旨在为这一事业开发新的理论和算法。该方法以以下对威胁检测问题的抽象描述为指导：在给定海量时空数据流的情况下，任务是维持一个缓慢演变的“正常”模型，任何偏离该模型的情况都将被进一步调查为潜在的威胁。该项目将专注于以下两个目标：(1)开发处理海量数据流的算法和优化编码，以及(2)开发快速证书和尖端学习算法保证。为此，本研究旨在解决优化、框架理论和机器学习中的一些大的开放问题：(A)从流数据中快速解决NP-Hard无监督学习问题的凸松弛；(B)构造最优行包装，包括Zauner猜想存在的包装；(C)为NP-Hard学习问题寻找次线性后验近似证书；(D)解释生成对抗网络所表现出的良好的优化场景；以及(E)为黑匣子分类制定快速、事后的解释，使人们能够在充分了解情况下做出决策。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。

National security interests demand a heightened awareness of the actions of various adversaries. This voracious appetite for information results in an overwhelming stream of spatiotemporal data. New mathematics is necessary to effectively manage this data deluge; this research project aims to develop new theory and algorithms for this cause. The approach is guided by the following abstract description of the threat detection problem: Given a massive stream of spatiotemporal data, the task is to maintain a slowly evolving model of "normalcy," any deviations from which are to be further investigated as potential threats. The project will focus on the following two objectives: (1) develop algorithms and optimal encodings to process massive data streams, and (2) develop fast certificates and guarantees for cutting-edge learning algorithms. To this end, the research aims to solve some of the big open problems in optimization, frame theory, and machine learning: (a) to quickly solve convex relaxations of NP-hard unsupervised learning problems from streaming data; (b) to construct optimal line packings, including the packings conjectured to exist by Zauner; (c) to find sub-linear a posteriori approximation certificates for NP-hard learning problems; (d) to explain the well-behaved optimization landscapes exhibited by generative adversarial networks; and (e) to develop fast, after-the-fact explanations for black-box classification, enabling well-informed human decision making.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.