ISI Directory

Heidi Morgan, Ph.D.

Computer Scientist, Senior

Education

Ph.D. Information Communication Technology, Erasmus University
M.A. English, Florida International University
B.S. Education, University of Miami

Bio

Heidi Morgan is a Senior Computer Scientist in the Networking and Cybersecurity Division at USC-ISI. Dr. Morgan moved to ISI in 2016 from Florida International University where she was the Director and Co-Founder of the Center for Internet Augmented Research and Assessment (CIARA). CIARA fosters a domain-specific tailoring of Internet technologies to support faculty research, and in the process improve graduate education.

Morgan serves as Co-Principal Investigator for several NSF sponsored projects involving collaboration on leading-edge network infrastructure for research and education between the U.S., Latin America, the Caribbean, and Africa. Heidi brings extensive experience utilizing advanced cyberinfrastructure to diverse groups of students, faculty, and career researchers in the science and education communities.

Research Summary

IRNC: Core Improvement: Americas-Africa Lightpaths Express and Protect (AmLight-ExP) (NSF Awards #2029283) Dec. 15, 2020 – Nov. 30, 2025, Big data science in South America and Sub-Saharan Africa will dramatically evolve over the next five years, with increasing dependency on advanced cyberinfrastructure and programmable networking. Significant projects include The Vera Rubin Observatory, the High Luminosity Large Hadron Collider experiments and the Square Kilometer Array in South Africa. AmLight-ExP supports high-performance network connectivity required by international science and engineering research and education collaborations involving the NSF research community, with expansion to South America and West Africa. AmLight-ExP increases the rate of discovery. Faster discovery means quicker focus on the greatest benefit for society. AmLight-ExP is a catalyst for new communities of researchers and learners with a bridge, linking U.S. Hispanic and African students, teachers and researchers. AmLight-ExP is committed to serving the needs of graduate and undergraduate education through models that bring together students and the networking community with scientists from all domains.

IRNC: Core Improvement: AtlanticWave-SDX: A Distributed Experimental SDX Supporting Research, Experimental Deployments, and Interoperability Testing on Global Scales (NSF Awards #2029278) Dec. 15, 2020 – Nov. 30, 2025 Open Exchange Points (OXPs) serve as meet points for connecting and facilitating the exchange of data between Research and Education (R&E) networks. They are critical cyberinfrastructure in the transit of data over long geographical distances, switching data flows from one R&E network to the next, to its destination. Operationalizing the transit of data flows across OXPs is increasingly important to minimize the impact from events on network services (hardware failures and soft failures). Florida International University (FIU), University of Southern California – Information Sciences Institute (USC-ISI) and University of North Carolina at Chapel Hill - RENCI are furthering AtlanticWave-SDX: a distributed experimental SDX, supporting research, experimental deployments, prototyping and interoperability testing, on national and international scales.

Vera Rubin Observatory Construction: Construction of the Vera Rubin Observatory under the MREFC. Intellectual Merit: Vera Rubin Observatory will map the inner and outer Solar System, study stellar populations in the Milky Way and nearby galaxies, revealing the structure of the Milky Way disk and halo and other local objects, find transient and variable objects at both low and high redshift, and survey the properties of normal and active galaxies at low and high redshift. Broader Impacts: The image archive and resulting catalogs will be widely and freely available. A sophisticated data management system will enable work ranging from simple queries from individual users to computationally intensive scientific investigations that could use the entire data set.