CAREER: Fast and Scalable Combinatorial Algorithms for Data Analytics

我们所处的时代，大量数字数据继续以极快的速度收集，复杂性（以及随之而来的不确定性）不断增加，数据及其背后的参与者越来越相互关联，计算平台的架构继续快速变化。为了分析海量数据集并从数据中提取知识和洞察力，迫切需要同时认识所有三个维度（数据，互连和计算平台）的快速，强大和可扩展的算法。该项目（名为FASCADA，快速和可扩展的数据分析组合算法）将探索图形和矩阵算法之间的相互作用，以开发在当代平台上大规模执行的数据分析方法，主要关注以网络表示的数据。在该项目中取得的化学研究进展将在软件实施中实现，将在适用时与现有软件工具相结合，并将作为开源软件提供给更广泛的社区。作为该项目综合教育和推广部分的一部分，将开发和教授两门新的创新课程，即数据科学本科课程和网络科学研究生课程。教育工作将有助于满足美国经济对训练有素的数据科学劳动力的快速增长的需求，并将有助于提高美国在全球市场的竞争力。在计算科学和工程的代表性不足的少数群体将通过现有的，有效的计划在太平洋西北部（LSAMP）招募和辅导，并从传统大学的本科生将通过夏季实习指导华盛顿州立大学。FASCADA的具体研究目标是根据四个相互交织的领域组织。(1)实现可扩展的数据分析：设计新颖的“问题划分”方法，这些方法对于大规模解决一类机器学习算法的优化问题非常有用。(2)网络分析：开发快速算法，用于发现和分析来自不同领域的真实网络中的稠密子图。(3)高性能计算：为面向众核架构的固有顺序图算法的并行化开发有效的范例。 (4)数学微分（AD）：通过为Hessian计算设计更好的基于图的算法来推进AD作为一种技术，并在新兴应用中使用AD，包括量化不确定性。贯穿这四个领域的一个共同点是关注图形问题及其解决方案。所提出的方法的新奇在于图和矩阵算法之间的双向交互的探索。这一努力的结果将推进一系列领域的交叉点的基础知识，包括数据科学，计算科学与工程，计算数学和高性能计算。欲了解更多信息，请访问项目网页http://www.eecs.wsu.edu/~assefaw/fascada。

We are in an age when massive digital data continues to be collected at an extraordinarily rapid rate and with high and growing complexity (and concomitant uncertainty), when the data and the actors behind it are increasingly interconnected, and when architectures of computing platforms continue to rapidly change. Fast, robust and scalable algorithms that are simultaneously cognizant of all three dimensions (data, interconnection, and computing platform) are acutely needed for the purpose of analyzing massive datasets and extracting knowledge and insight from the data. This project (named FASCADA, Fast and Scalable Combinatorial Algorithms for Data Analysis) will explore the interplay between graph and matrix algorithms in order to develop methods for data analytics that perform at scale on contemporary platforms, with a primary focus on data that are expressed in terms of networks. Algorithmic research progress to be made in the project will be realized in software implementations, will be integrated with existing software tools when applicable, and will be made available to the wider community as open-source software. As part of the project's integrated education and outreach component, two new innovative courses, an undergraduate course on Data Science and a graduate course on Network Science, will be developed and taught. The educational effort will contribute to meeting the rapidly expanding need for a trained workforce in data science in the US economy and will contribute to US competitiveness in the global market. Underrepresented minority groups in computing sciences and engineering will be recruited and mentored through an existing, effective program in the Pacific Northwest (LSAMP), and undergraduate students from Heritage University will be mentored through summer internships at Washington State University.The specific research aims of FASCADA are organized under four intertwined areas. (1) Enabling Scalable Data Analytics: devise novel "problem-partitioning" methods that are useful for solving, at scale, optimization problems underlying a large class of machine learning algorithms. (2) Network Analysis: develop fast algorithms for discovering and analyzing dense subgraphs in real-world networks arising from diverse domains. (3) High Performance Computing: develop effective paradigms for the parallelization of inherently sequential graph algorithms targeting many-core architectures. (4) Algorithmic Differentiation (AD): advance AD as a technology by designing better graph-based algorithms for Hessian computation, and use AD in emerging applications, including quantifying uncertainty. A common thread that runs through all four of the areas is a focus on graph problems and their solution. The novelty of the proposed approach lies in the exploration of the bidirectional interaction between graph and matrix algorithms. Results from this effort will advance fundamental knowledge at the intersection of a range of areas, including data science, computational science and engineering, computational mathematics, and high performance computing. For further information, visit the project webpage http://www.eecs.wsu.edu/~assefaw/fascada.