Collaborative Research: GOALI Statistical Methods for Modern IT Systems

受IT行业真实的问题的启发，如数据中心/超级计算机热管理和业务运营模拟，研究人员的目标是开发一套新颖的统计方法，用于现代IT系统的实验规划，建模和优化。 具体来说，他们提出了新的建模技术，为IT系统的高维响应，空间和时间的响应，或与大量的配置变量，并为实验规划和优化业务操作模拟。研究人员汇集了计算机建模和实验，潜变量和结构方程模型，现代优化和实验设计方面的专业知识。他们的研究将弥合企业和工业统计实践之间的差距。他们将在IBM使用具有挑战性的现实问题来激励和开发新的方法，并反过来在真实的数据上测试它们以进行改进。 它的智力价值在于开发了一种通用方法，用于设计，建模和优化IT或其他表现出与上述相似特征的系统。它可以导致在多变量统计建模，随机过程建模，辅助随机优化，优化辅助设计搜索和空间填充设计的重大进展。可以想象，该方法可以被纳入公开发布的软件，从而直接使从业人员和研究人员受益。许多新的IT系统，如超级计算机、数据中心和存储系统，已经被发明出来，以适应不同的业务和个人需求。IT行业最近的一个趋势是将传统的硬件业务与企业IT服务（如Web托管、数据存储和高性能计算）相集成。预计拟议的工作将对复杂系统的设计、监测和管理产生广泛影响。它可以应用于各种问题，如电力系统和飞机发动机燃烧室。它还可以用于气候变化的大规模计算机建模，对自然危机的反应，以及禽流感和SARS等流行病的传播。 具体而言，所开发的方法可以提高电子系统的热管理和冷却效率，这是当今许多美国行业面临的紧迫问题。该小组致力于教育和传播新方法，以产生长期影响。他们计划将研究成果融入课程。拟议的教育计划将导致严格的培训，在统计方法，不确定性下的决策和计算建模，谁将满足IT和其他高科技行业的关键需求坚实的背景的学生的多样化。他们致力于在他们的研究小组中创造一个种族，性别和民族血统方面的多元化环境。

Motivated by real problems in the IT industry like data center/supercomputer thermal management and simulations of business operations, the investigators aim to develop a set of novel statistical methods for the experimental planning, modeling, and optimization of modern IT systems. Specifically, they propose new modeling techniques for IT systems with high-dimensional responses, with spatial and temporal responses, or with a large number of configuration variables, and for the experimental planning and optimization of business operation simulations. The investigators bring together expertise in computer modeling and experiments, latent variable and structural equation models, modern optimization, and design of experiments. Their research will bridge the gap between statistical practice in academe and industry. They will use challenging real-world problems at IBM to motivate and develop new methodologies, and in turn test them on real data for improvement. Its intellectual merit lies in the development of a general methodology for designing, modeling, and optimizing IT or other systems which exhibit similar traits to those described above. It can lead to major advances in multivariate statistical modeling, stochastic process modeling, statistics-aided stochastic optimization, optimization-aided design search, and space-filling designs. It is conceivable that the methodology can be incorporated into publicly released software, thus directly benefiting practitioners and researchers. Many new IT systems like supercomputers, data centers, and storage systems have been invented to accommodate different business and personal needs. A recent trend in the IT industry is the integration of the traditional business in hardware with enterprise IT services like web hosting, data storage, and high-performance computing. The proposed work is expected to have broad-based impacts on the designing, monitoring and management of complex systems in general. It can be applied to a variety of problems like power systems and aircraft engine combustors. It can also benefit large-scale computer modeling for climate change, reaction to natural crisis, and the spread of pandemic diseases like bird flu and SARS. Specifically, the developed methods can improve the thermal management and cooling efficiency of electronic systems, which is a pressing issue faced by many US industries today. The team is committed to education and dissemination of the new methodology to make a long-term impact. They plan to infuse research outcomes into coursework. The proposed education plan will result in rigorous training of a diverse group of students with solid background in statistical methods, decision-making under uncertainty, and computational modeling, who will meet a critical need of IT and other high-tech industries. They are committed to creating a diverse environment in their research groups in terms of race, gender and national origin.