CAREER: Scalable Software and Algorithmic Infrastructure for Probabilistic Graphical Modeling

数据驱动推理是推动科学和工程进步的主要因素之一。在许多实际应用中，特别是在生物学和医学中，数据驱动推理一直基于概率图模型，由于数据表示的准确性和易于解释而受到青睐。在概率图形建模中，被建模的对象，例如，存储在电子健康记录中的患者的属性，被表示为随机变量，目标是学习这些变量之间的依赖关系。然而，从数据中学习高质量概率图模型的方法在计算上具有挑战性，并且不能扩展到现代应用中出现的数据集。因此，该项目旨在通过使用高性能计算技术来实现大型数据集的高质量概率图形建模。为此，该项目引入了概率图形建模基础操作的框架，包括管理数据和协调计算，以及它们的软件实现，可以有效地利用大规模并行计算机。该框架旨在使对大规模数据分析感兴趣的从业者和对新学习算法开发感兴趣的研究人员都受益。该框架的验证和评估是基于对电子健康记录的分析，目标是对慢性阻塞性肺疾病患者进行早期预后和诊断-这是提高质量和降低医疗成本的关键问题。此外，该框架为培训下一代生物医学专业人员在先进的网络基础设施上使用数据分析提供了基础。因此，该项目符合NSF的使命，以促进科学的进步，并推进国家的健康，繁荣和福利。该项目响应了公认的和不断增长的需求，可扩展的机器学习方法，可以利用并行架构，如大型集群的多核处理器。研究重点是概率图模型的精确结构学习，例如贝叶斯网络和马尔可夫随机场，在生物医学数据分析的背景下。该项目基于两个主要组成部分：用于管理机器学习应用程序中的数据的新的高性能抽象，包括用于在多核处理器上回答计数查询的内存高效策略，以及用于分布式内存系统的新编程模型，以促进大规模组合搜索空间的有效探索，例如由树，格或图描述的空间。这些抽象被用来实现一组新的并行，精确的算法结构搜索，以及相关的问题，例如马尔可夫毯识别，加速学习，利用各种属性的输入数据和底层的搜索空间。研究部分是由个性化和预防医学的及时和社会相关的应用驱动的，通过大量收集实际的电子健康记录来实现。该项目旨在提供多个工件，包括基于MPI和OpenMP的软件，基准数据和教育材料，所有这些都作为开源发布，供社区使用，进一步开发，增强和合并。研究活动与多种教育工作紧密结合，跨越为医学专业人员开发跨学科课程，培训他们使用先进的网络基础设施，本科生和代表性不足的少数民族参与研究，和推广到初中和高中学生，以吸引他们到干。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用的支持基金会的学术价值和更广泛的影响审查标准。

The data-driven reasoning is one of the major factors propelling progress in science and engineering. In many practical applications, especially in biology and medicine, data-driven reasoning has been based on probabilistic graphical models, that are preferred because of the accuracy in data representation and ease of interpretation. In probabilistic graphical modeling, the modeled objects, for example, the attributes of a patient stored in electronic health records, are represented as random variables, and the goal is to learn dependencies between these variables. However, the methods to learn high-quality probabilistic graphical models from the data are computationally challenging, and do not scale to datasets emerging in modern applications. Therefore, this project aims to enable high-quality probabilistic graphical modeling of large datasets by using high performance computing techniques. To this end, the project introduces a framework of fundamental operations underlying probabilistic graphical modeling, including for managing data and coordinating computations, together with their software fulfillment, that can efficiently leverage large-scale parallel computers. The framework is designed to benefit both practitioners interested in the analysis of large-scale data, and researchers interested in the development of new learning algorithms. The validation and evaluation of the framework is based on the analysis of electronic health records with the goal of early prognosis and diagnosis of patients with chronic obstructive pulmonary disease - problems vital for improving quality and reducing the cost of healthcare. Furthermore, the framework provides the foundation to train the next generation of biomedical professionals in the use of data analytics on advanced cyberinfrastructure. Thus, the project is aligned with NSF's mission to promote the progress of science, and to advance the national health, prosperity and welfare.This project responds to the recognized and growing demand for scalable machine learning methods that could capitalize on parallel architectures such as large clusters of multi-core processors. The research focus is on exact structure learning of probabilistic graphical models, for example Bayesian networks and Markov random fields, in the context of biomedical data analytics. The project is based on the two main components: a new high performance abstraction for managing data in machine learning applications, including memory efficient strategies for answering counting queries on multi-core processors, and a new programming model for distributed memory systems to facilitate efficient exploration of large-scale combinatorial search spaces, such as those described by tress, lattices or graphs. These abstractions are used to realize a set of new parallel, exact algorithms for structure search, and the related problems, for example Markov blankets identification, that accelerate learning by exploiting various properties of the input data and the underlying search spaces. The research component is driven by the timely and socially relevant application in personalized and preventive medicine, enabled by a massive collection of the actual electronic health records. The project aims to delivers multiple artifacts, including MPI and OpenMP-based software, benchmark data and educational materials, all released as open source for use, further development, enhancement, and incorporation by the community. The research activities are tightly coupled with multiple educational efforts, spanning development of an interdisciplinary course for medical professionals to train them in the use of advanced cyberinfrastructure, engagement of undergraduate students and underrepresented minorities in research, and outreach to middle and high school students to attract them to STEM.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.