MRI: Acquisition of High Performance Computing Infrastructure to Support Computational and Data-Enabled Science and Engineering

该项目将获得高性能的数据分析和计算基础设施，以支持布法罗大学(UB)在计算和数据启用的科学与工程(CDSE)方面的新的跨学科博士项目，以及学生/教师在数据与计算接口方面的研究。具体地说，拟议的资源将使整个密歇根大学能够进行广泛的跨学科研究，涉及来自15个部门的25名附属教员。考虑到大数据、数据分析和基于模拟的工程与科学(SBES)对社会、科学和经济的重要性日益增长，以及确保发展一支具有使美国处于信息时代前沿所需技能的充足劳动力的迫切需要，CDSE项目中的资源整合尤为及时。特别是，收购的基础设施将支持广泛领域的研究和教育，如：分子体系光谱、光学和磁性模拟的量子理论方法的开发和应用；金融市场微观结构和资产定价；大数据的机器学习和数据挖掘方法；地理信息系统；对上升到大气中的火山喷发和火山灰羽流和火山灰云进行建模；大规模计算和数据科学的新方法的整合，以解决从质量流动到生物力学的各种问题；调控基因组学、基因调控、基因组注释；定量网络神经科学；快速而准确地探索大型生物和工程数据集合；以及药代动力学/药效学建模和大数据的垂直整合以识别目标。此外，获得的基础设施将为CDSE毕业生提供计算、数学和统计技能，这些技能在数据科学、应用数学和数值数学以及高性能和数据密集型计算领域提供深度和广度。通过促进这些即将毕业并在全国各地工作的“下一代”数据分析师和计算科学家的培训，该项目将在金融、工程、材料科学、地质、医学和经济等广泛领域的知识和理解方面产生远远超出亚利桑那州边界的影响。

This project will acquire high-performance data analytic and computing infrastructure to support the University at Buffalo's (UB) new cross-disciplinary PhD program in Computational and Data-Enabled Science and Engineering (CDSE), as well as student/faculty research at the interface of data and computing. Specifically, the proposed resource will enable a broad range of cross-disciplinary research throughout UB, involving 25 affiliated faculty members from across 15 departments. The integration of the resource in the CDSE program is particularly timely given the growing importance that big data, data analytics and simulation based engineering and science (SBES) have on society, science and the economy, as well as the critical need to ensure the development of an adequate workforce with the skills necessary to keep the United States at the forefront of the information age. In particular, the acquired infrastructure will support research and education in a wide range of areas, such as: the development and application of quantum theoretical methods for the simulation of spectroscopic, optical, and magnetic properties of molecular systems; financial market microstructure and asset pricing; machine learning and data mining methodologies for big data; geographic information systems; modeling volcanic eruptions and volcanic ash plumes and ash clouds that rise into the atmosphere; the integration of novel methodologies for large scale computational and data sciences to solve problem ranging from mass flows to biomechanics; regulatory genomics, gene regulation, genome annotation; quantitative network neuroscience; fast and accurate exploration of large biological and engineering data collections; and pharmacokinetic/pharmacodynamics modeling and vertical integration of big-data for target identification. Furthermore, the acquired infrastructure will provide CDSE graduates with computational, mathematical and statistical skills that provide both depth and breadth across the areas of data sciences, applied and numerical mathematics and high performance and data intensive computing. By facilitating the training of these "next generation" data analysts and computational scientists, who will graduate and take jobs throughout the Nation, the project will have an impact far beyond the boundaries of UB in terms of advancing knowledge and understanding across a broad spectrum of fields, including finance, engineering, materials science, geology, medicine, and economics.