CAREER: Statistically-Sound Knowledge Discovery from Data

从数据中发现知识的方法（例如，提取模式或发现异常）已经在生命和生物科学的研究实验室以及网络安全等行业中找到了自己的方法。在这些领域，这些方法产生的结果的统计有效性是至关重要的：错误的发现是不能容忍的。目前的方法不能提供如此严格的统计保证。该项目开发了从数据中发现统计可靠知识的算法。它通过将知识发现过程的目标从提取有关可用数据的信息转变为获得对生成数据的嘈杂、随机过程的新理解，从而改变了这个领域。通过允许科学家和从业者有效地分析丰富的大型数据集并信任分析结果，所提出的方法有助于实现更快，更高通量的科学管道。这样，研究人员就可以专注于他们特定学科的研究任务，而不必担心计算或统计方面的考虑。该项目包括与当地博物馆和当地公共图书馆合作，分析其历史材料收藏的数据，并与网络安全公司合作开发快速检测网络攻击的方法，减少误报。不同的本科生群体将参与该项目的研究和教育部分。知识发现的研究主要集中在理解可用数据，而不是产生数据的过程。在使用假设检验来评估结果的少数情况下（主要是简单模式），只考虑简单的零模型，并且测试采用低统计功率方法（例如，Bonferroni校正）来控制错误发现的一个测量，即家庭明智错误率。该项目具有变革性，因为它将开发有效的方法来评估从大型丰富数据集（例如，事务数据集，图形和时间序列）获得的各种结果（例如，模式，异常，图形/顶点/边缘属性等），使用更适合这些任务的现实null模型，并更好地编码数据生成过程的可用知识。我们将创建新的有效程序来从这些模型中采样，包括近似（例如，马尔可夫链蒙特卡罗）和精确，并将它们与现代基于重采样的多重测试方法相结合，以多假设优先的方法也控制（边际）错误发现率。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。

Methods for knowledge discovery from data (e.g., for extracting patterns or finding anomalies) have found their way to research labs in life and biological sciences, and in industries such as cybersecurity. In these fields, the statistical validity of the results produced by these methods is paramount: false discoveries cannot be tolerated. Current methods do not offer such stringent statistical guarantees. This project develops algorithms for statistically-sound Knowledge Discovery from Data. It transforms the field by shifting the goal of the Knowledge Discovery process from extracting information about the available data to gaining new understanding of the noisy, random process that generates the data. The proposed methods contribute towards a faster and higher-throughput scientific pipeline, by allowing scientists and practitioners to efficiently analyze rich large datasets and to trust the results of the analysis. Researchers can then focus on their discipline-specific research tasks without worrying about computational or statistical considerations. The project includes collaborations with a local museum and a local public library, to analyze data about their collections of historic materials, and with a cybersecurity company to develop methods for fast detection of network attacks with few false positives. A diverse cohort of undergraduate students will be involved in the research and educational components of the project.Research in knowledge discovery has mostly focused on understanding the available data, rather than the process that generated it. In the few cases where hypothesis testing was used to assess the results (mostly for simple patterns), only simplistic null models were considered, and the testing employed low-statistical-power approaches (e.g., the Bonferroni correction) to control only for one measure of false discovery, the Family-Wise Error Rate. This project is transformative because it will develop efficient methods for evaluating a wide variety of results (e.g., patterns, anomalies, graph/vertex/edge properties, and more) obtained from large rich datasets (e.g., transactional datasets, graphs, and time series), using realistic null models which are more appropriate for these tasks, and better encode available knowledge of the data generating process. We will create novel efficient procedures to sample from such models, both approximate (e.g., Markov-Chain Monte Carlo) and exact, and combine them with modern resampling- based multiple testing methods, in a multiple-hypothesis first approach that also controls the (marginal) False Discovery Rate.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.