MRI: Acquisition of a Computing Cluster to Enable Transformative Research across Disciplines

Loyola University Maryland, Inc., Baltimore MD

Investigators

Abstract

Loyola University Maryland will acquire a computing cluster with central processing and graphical processing units (CPU and GPU). The computing cluster will be utilized for computationally expensive research projects spanning multiple fields, including computer science, materials engineering, electrical engineering, physics, chemistry, and economics. As a primarily undergraduate institution, Loyola is also committed to training the next generation of researchers via undergraduate research opportunities. Through the acquisition of its first major computing cluster, Loyola will expand opportunities to introduce undergraduate students to modern computationally-intensive tasks and multidisciplinary research, preparing them for graduate school and computational work within their chosen fields. Additionally, it will support projects within the university's new interdisciplinary Data Science M.S. degree program. The initial eleven research projects will impact areas as diverse as software development, medicine, and social policy. Example impacts include developing techniques leading to drugs to fight SARS; understanding protein-DNA interactions for biotechnology applications; providing policymakers with a better understanding of how job competition and human capital allocation influence the optimal design of unemployment insurance; improving software quality by helping programmers identify and avoid the introduction of dependence clusters; improving techniques for coordination in pursuing a moving target, such as in military operations, autonomous automobile police chases, or surveillance programs; new methods for the creation of fossil fuel alternatives; providing secure communications for mobile devices in the Internet of Things; and improving techniques for nondestructive evaluation of electromagnetic materials, which is of critical importance in agriculture, bio-electromagnetics, aerospace, and the design of integrated circuits. This high performance computing cluster will significantly increase the shared computing resources on campus, thereby expanding opportunities for faculty research, faculty recruitment, and student research.