Dr. Erin Fitzgerald leads the ARLIS-led INtelligence and Security University Research Enterprise (INSURE), building a robust consortium of universities to further ARLIS’s mission and impact as the Department of Defense designated University-Affiliated Research Center (UARC) for securing the human domain. Inspired by Joy's Law ("No matter who you are, most of the smartest people work for someone else"), the INSURE network helps ARLIS respond with agility and a deep bench of expertise to address problems the security and intelligence communities bring its way.
Erin also served as ARLIS Director for Operations and Chief of Staff, providing daily leadership for all operational, strategic planning, business development, and personnel activities related to the operation and management of ARLIS. Previously, Erin supported the UMD Vice President for Research as the Director of National Research Initiatives, working to identify and cultivate high visibility research opportunities and relationships between UMD researchers and various external funding sources and partners.
Erin joined the University of Maryland in December 2016, coming from the Office of the Secretary of Defense (OSD) where she served dual roles as a Strategist for OSD Policy and Senior Science Advisor to the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering. From 2010-2016, Erin directed OSD’s Minerva Research Initiative, a $30M defense social science research program with a portfolio ranging from the mechanisms of radicalization to the role of energy and the environment in shaping societal resilience and geopolitical power projection in a multipolar world. As science advisor, she also developed strategic plans for future basic research investments across the defense enterprise.
Erin received her B.S. in electrical and computer engineering from Carnegie Mellon University and her master’s and Ph.D. degrees in electrical and computer engineering from The Johns Hopkins University. Her research in speech and language processing combined electrical engineering, computer science, and cognitive science approaches for data-driven efforts in automatic speech recognition and language translation.