MRI: ACQUISITION OF A GPU CLUSTER TO SUPPORT INTERDISCIPLINARY RESEARCH IN HUMAN LEARNING, MACHINE LEARNING, AND DATA SCIENCE

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.