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William A. Ingram, Assistant Professor for University Libraries at Virginia Tech, is an internationally recognized digital libraries scholar specializing in computational use of library collections. He is Assistant Dean and Director of Information Technology for University Libraries.

Ingram's research involves devising methods for computational analysis and use of the large digital collections held by libraries and archives. He is especially interested in electronic theses and dissertations. Because they are long and contain multiple chapters, they more resemble books than research articles. His aim is to use methods for information extraction, classification, and summarization to uncover the knowledge and data buried in this rich corpus of graduate research to facilitate its discovery, use, and reuse.

Ingram received a B.A. in cognitive science from the University Of Virginia and an M.S. in Library and Information Science from the University of Illinois at Urbana-Champaign in 2008. Since then, he has been involved in projects and services related to scholarly communication, digital preservation, repositories and digital libraries. He is currently pursuing a Ph.D. in Computer Science at Virginia Tech, under the supervision of Professor Edward A. Fox, with a dissertation focused on the application of NLP and machine/deep learning to large-scale scholarly data. In 2019, he was awarded a 3-year National Digital Infrastructures research grant from the Institute of Museum and Library Services (LG-37-19-0078-19-0) to study the application of computational methods and resources to large corpora of electronic theses and dissertations.

News

  • August 2021 One short and one long paper accepted at JCDL'21.
  • April 2021 VT-NARA workshop kicks off. Sponsored by the Andrew W. Mellon Foundation, Virginia Tech and the U.S. National Archives and Records Administration are hosting a workshop series on "Ensuring Scholarly Access to Government Records and Archives" April 9, 16, 23, 30, and May 7. Visit https://lib.vt.edu/research-teaching/computational-archives-workshop.html for updates and https://www.archives.gov/developer/artificial-intelligence-and-machine-learning-datasets for program informaion.
  • February 2021 Project briefing webinar accepted for CNI’s Spring 2021 Virtual Meeting.
  • November 2020 VT-NARA workshop rescheduled for Spring '21, will be held virtually.
  • October 2020 Project briefing webinar accepted for CNI’s Fall 2020 Virtual Meeting.
  • April 2020 Poster accepted at JCDL'20.
  • April 2020 Paper accepted for Practitioners Track at JCDL'20.
  • March 2020 VT-NARA workshop postponed due to COVID-19.
  • February 2020 Proposal accepted for half-day tutorial on Preparing Code and Data for Computational Reproducibility at JCDL'20.
  • February 2020 Article accepted in Data and Information Management journal.
  • January 2020 Grant awared (1910-07229) for "Ensuring Scholarly Access to Government Records and Archives" by Andrew W. Mellon Foundation to support a convening of experts to address machine-learning techniques to enhance public access to government records.
  • October 2019 Nominated to join NDLTD Board of Directors.
  • October 2019 Project briefing accepted for the CNI meeting in Washington, DC on December 9-10, 2019.
  • August 2019 Paper on ETDs and workshop on computational reproducibility accepted for ETD 2019.
  • June 2019 Received National Leadership Grants for Libraries Program award (LG-37-19-0078-19) for the project "Opening Books and the National Corpus of Graduate Research."