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）在计算和数据支持科学与工程（CDSE）新的跨学科博士课程，以及学生/教师在数据和计算界面的研究。 具体而言，拟议的资源将使整个UB的跨学科研究范围广泛，涉及来自15个部门的25名附属教员。考虑到大数据，数据分析和基于仿真的工程和科学（SBES）对社会，科学和经济的日益重要性，以及确保发展足够的劳动力的迫切需要，CDSE计划中的资源整合特别及时。特别是，收购的基础设施将支持广泛领域的研究和教育，例如：开发和应用量子理论方法模拟分子系统的光谱，光学和磁性;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;建模火山爆发和火山灰羽流和灰云上升到大气中;大规模计算和数据科学的新方法的整合，以解决从质量流到生物力学的问题;调控基因组学，基因调控，基因组注释;定量网络神经科学;快速准确地探索大型生物和工程数据集;以及药代动力学/药效学建模和大数据的垂直整合，用于靶点识别。 此外，收购的基础设施将为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“下一代”数据分析师和计算科学家的培训，他们将毕业并在全国各地工作，该项目将在促进知识和理解方面产生远远超出UB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.