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William A. Ingram is an associate professor at Virginia Tech, where he serves as Associate Dean and Executive Director for IT for the University Libraries and Director of the Center for Digital Research and Scholarship .

Ingram's research is centered on the integration of AI and machine learning with big data applications within academic and scholarly contexts. Specifically, he focuses on employing natural language processing to enrich digital library systems and scholarly data management. Ingram's work aims to develop scalable, ethical technical solutions that align with the strategic goals of higher education institutions and digital archives, emphasizing innovation and the responsible use of AI in knowledge management.

Ingram received a B.A. in cognitive science from the University of Virginia and an M.S. in Library and Information Science from the Graduate School of Library and Information Science 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-scalescholarly big data.

News

  • Our paper “VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models” has been accepted for publication in the NAACL 2025 Main Conference. Read more at DOI: 10.48550/arXiv.2411.04825.
  • Three of our posters and one workshop paper have been accepted at the 2024 IEEE International Conference on Big Data (IEEE BigData 2024).
  • Our workshop paper, "Automating Chapter-Level Classification for Electronic Theses and Dissertations," was accepted at the 9th Computational Archival Science (CAS) Workshop, part of the 2024 IEEE Big Data Conference (IEEE BigData 2024). Contratulations Bipasha!
  • I have been awarded continued appointment and promotion to Associate Professor at Virginia Tech.
  • Our journal article "Building Datasets to Support Information Extraction and Structure Parsing from Electronic Theses and Dissertations" has been published in the International Journal on Digital Libraries.
  • Our paper titled "ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations" has been accepted for presentation at the Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24).
  • Our long paper, "Integrated Digital Library System for Long Documents and their Elements," has been nominated for Best Student Paper Award at JCDL 2023.
  • Our short paper, "MetaEnhance: Metadata Quality Improvement for Electronic Theses and Dissertations of University Libraries," is a Best Short Paper nominee at JCDL 2023.
  • Panel proposal accepted at JCDL 2023, "AI and Public Archives: Collaborative Leadership for Responsible Adoption."
  • Paper accepted at Sci-K 2023, co-located with The Web Conf 2023. Congratulations Aman!
  • Paper accepted at the 7th Computational Archival Science (CAS) Workshop, part of IEEE Big Data 2022. Congratulations Bipasha!
  • Paper accepted at the ETD 2022 Symposium in Novi Sad, Serbia. Congratulations Aman!
  • I will be co-leading a workshop in May on Leading the Future of AI and Public Archives with NARA, Library of Congress, and the Smithsonian.
  • Paper accepted by 2nd International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K) co-located with the 2022 Web Conference. Congratulations Lamia!
  • Report on Virginia Tech-NARA workshop series on AI deposited in VTechWorks. http://hdl.handle.net/10919/108067
  • Poster accepted at IEEE BigData. Congratulations Sami!
  • One short and one long paper accepted at JCDL'21.
  • 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.
  • Project briefing webinar accepted for CNI's Spring 2021 Virtual Meeting.
  • VT-NARA workshop rescheduled for Spring '21, will be held virtually.
  • Project briefing webinar accepted for CNI's Fall 2020 Virtual Meeting.
  • Poster accepted at JCDL'20.
  • Paper accepted for Practitioners Track at JCDL'20.
  • VT-NARA workshop postponed due to COVID-19.
  • Proposal accepted for half-day tutorial on Preparing Code and Data for Computational Reproducibility at JCDL'20.
  • Article accepted in Data and Information Management journal.
  • 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.
  • Nominated to join NDLTD Board of Directors.
  • Project briefing accepted for the CNI meeting in Washington, DC on December 9-10, 2019.
  • Paper on ETDs and workshop on computational reproducibility accepted for ETD 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."