MRI: Acquisition of a GPU cluster to support interdisciplinary research in human learning, machine learning, and data science

这项重大仪器补助金支持收购GPU集群，以支持罗格斯大学-纽瓦克少数民族服务机构（MSI）在人类学习、机器学习和数据科学方面的跨学科研究。它允许购买3个Nvidia双V100 GPU，以实现跨学科学习理解的理论进步和实际应用。罗格斯-纽瓦克大学正在进行一项为期多年的努力，以建立跨学科计算机科学的实力，以支持研究培训，并解决计算机科学和数据科学中的多样性和代表性问题。这些资源将：（1）支持计算密集型方法的应用，以开发新的理论和工具来理解人类和机器学习;（2）支持现有的跨学科培训工作，例如以深度学习和深度高斯过程为中心的研究生课程;（3）通过允许部署先进的数据分析方法来加强现有的资助研究。GPU集群将为计算机科学、心理学和神经科学部门的研究人员提供一个通用的计算资源，通过这些资源，他们可以合作推进每个领域的最新技术。此次购买将补充校园内现有的高性能计算基础设施，以及最近由NSF支持的1.2 PB存储系统的购买，用于对人类视觉体验的动态进行编目。此外，它将补充NSF赞助的移动的制造商中心，以社区为基础的数据收集和功能磁共振成像研究。人类仍然是最强大和最令人印象深刻的学习模型，尽管这些能力的根源还没有完全理解。尽管机器学习方法近年来变得异常强大，但它们仍然不透明，人类学习并不如此，并且仍然需要比人类学习者更多的数据，能源和计算能力。人类和机器学习都将受益于更紧密地联系和研究各自优势的能力。高斯过程提供了这样一个统一的框架。它们是机器学习中感兴趣的对象，在机器学习中，它们具有回归模型和神经网络的双重解释，以及在人类学习中，它们被提出作为认知和感知的模型。这些对高斯过程的多种解释是它们连接人类和机器学习的关键。从理论角度来看，高斯过程相当于（一种特定类型的）神经网络，但更适合数学分析，并且可以堆叠以获得深度高斯过程。这种深度学习框架可以允许比其他深度学习方法更系统的数学分析-例如，能够为他们的推论得出解释。该项目的主要研究目标是利用GPU集群和研究人员的跨学科专业知识，在机器学习和人类学习视角之间建立深层联系，以推进两者的最新技术水平，该奖项反映了美国国家科学基金会的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查进行评估的支持的搜索.

This Major Instrumentation Grant award supports the Acquisition of a GPU cluster to support interdisciplinary research in human learning, machine learning, and data science at Rutgers University--Newark, a Minority Serving Institution (MSI). It permits purchase of 3 Nvidia dual V100 GPUs to enable theoretical advances and practical applications in interdisciplinary understanding of learning. Rutgers-Newark is undertaking a multiyear effort to build strength in interdisciplinary computer science to support research training, and to address issues of diversity and representation within computer science and data science. These resources would: (1) enable the application of computationally-intensive methods in order to develop new theories and tools to understand human and machine learning; (2) support existing cross-disciplinary training efforts, such as graduate-level courses centered around deep learning and Deep Gaussian Processes; (3) enhance existing funded research by allowing the deployment of advanced data-analytic methods. The GPU cluster will provide a common computational resource for researchers from the Computer Science, Psychology, and Neuroscience departments through which they may collaborate to advance the state-of-the-art in each field. This purchase will complement the existing high-performance computing infrastructure already on campus as well as a recent NSF-supported purchase of a 1.2 petabyte storage system for cataloging the dynamics of human visual experience. Also, it will supplement an NSF-sponsored Mobile Maker Center for community-based data collection and fMRI research. Humans remain the most powerful and impressive available models of learning, although the roots of these abilities are not fully understood. Although machine learning methods have become exceptionally powerful in recent years, they remain opaque in ways that human learning is not and still require vastly more data, energy and compute power than human learners. Both human and machine learning would benefit from the ability to more tightly connect and study the strengths of each. Gaussian processes provide one such unifying framework. They are an object of interest in machine learning, where they have dual interpretations as regression models and as neural networks, as well as in human learning where they have been proposed as models of cognition and perception. These multiple interpretations of Gaussian processes are key to their interest for bridging human and machine learning. From a theoretical perspective, Gaussian processes are equivalent to (a specific type of) neural network, but much more amenable to mathematical analysis, and can be stacked to obtain Deep Gaussian processes. This Deep learning framework may allow more systematic mathematical analysis than other Deep learning approaches---for example the ability to derive explanations for their inferences. The primary research goal of this project is to use the GPU cluster and the investigators' interdisciplinary expertise to draw deep connections between machine learning and human learning perspectives to advance the state of the art in both, while also improving data analytic capabilities.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.