Research Projects

  

 

III: Small: Bringing Transparency and Interpretability to Bias Mitigation Approaches in Place-based Mobility-centric Prediction Models for Decision
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project focuses on improving the fairness of place-based mobility-centric (PBMC) prediction models, particularly in high-stakes scenarios like public health and safety. By addressing biases in COVID-19 mobility and case data, it aims to make predictions more accurate and equitable. The research introduces novel bias-mitigation and interpretability methods across three technical thrusts, promoting transparency in PBMC models.
Inclusive ICT RERC
Principal Investigator(s): Gregg Vanderheiden J. Bern Jordan Hernisa Kacorri Amanda Lazar Jonathan Lazar
Funder: HHS / ACL / National Institute on Disability, Independent Living, and Rehabilitation Research Other
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Human-Computer Interaction > Information Justice, Human Rights, and Technology Ethics
Ensuring that existing information and communication technologies (ICT) solutions for people with disabilities are known, effective, findable, more affordable, and available on every computer or digital technology platform; and exploring the emerging next-next-generation interface technologies for which there are no effective accessibility guidelines or standards, and problem-solving in advance of these technologies.
Inverting Colonial Archival Structures: Increasing Discovery and Access for Indigenous Communities through SNAC
Principal Investigator(s): Diana E. Marsh
Funder: Institute of Museum and Library Services Other
Research Areas: Accessibility and Inclusive Design > Archival Science > Digital Humanities > Library and Information Science > Social Networks, Online Communities, and Social Media
Inverting Colonial Archival Structures: Increasing Discovery and Access for Indigenous Communities through SNAC (Indigenize SNAC) aims to test discovery and access of archival records for indigenous communities through the web platform Social Networks for Archival Contexts (SNAC). The project is funded by the IMLS Laura Bush 21st Century Librarian program.
Investigating the Information Practices of COVID Long-Haulers
Principal Investigator(s): Beth St. Jean Twanna Hodge Jane Behre J. Nicole Miller
Funder: UMD Impact Award - Pandemic Readiness Initiative: https://research.umd.edu/pri Other
Research Areas: Health Informatics > Information Justice, Human Rights, and Technology Ethics > Library and Information Science
This project investigates the information needs, practices, and experiences of people who have long COVID ("COVID long-haulers") in order to learn more about their COVID-related information needs, the ways in which they have gone about fulfilling these needs, and their information-related experiences. W
Launching the TALENT Network to Promote the Training of Archival & Library Educators w. iNnovative Technologies
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The TALENT Network (Training of Archival & Library Educators with iNnovative Technologies) brings together experts from across the United States (including archivists, librarians, Library and Information Science educators, historians, learning scientists, cognitive scientists, computer scientists, and software engineers) in order to create a durable, diverse, and multidisciplinary national community focused on developing digital expertise and leadership skills among archival and library educators.
Libraries, Integration, and New Americans: Understanding immigrant acculturative stress
Principal Investigator(s): Ana Ndumu
Funder: Institute of Museum and Library Services
Research Areas: Information Justice, Human Rights, and Technology Ethics > Library and Information Science
Libraries, Integration, and New Americans,” or L.I.N.A., is a three-year research project directed by Dr. Ana Ndumu that will answer the following questions: What is the role of information in immigrant acculturative stress? How does information-related acculturative
stress impact library access? How can libraries help adult immigrants who are overwhelmed by information? Funding from IMLS under the Laura Bush 21st Century Early Career.
Long Term Multi-Instruments Land Surface Reflectance Record and Applications
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Smart Cities and Connected Communities
The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products.
M3I – Maps, Models, and Metrics for Influence Efforts by State/Non-State Actors
Principal Investigator(s): Cody Buntain
Funder: DOD-DARPA-Defense Advanced Research Projects Agency Other

Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Mapping Inequality — Redlining in New Deal America
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science
Providing online access to the totality of the maps and neighborhood descriptions for the national redlining collection.
Maryland Sports Data Analytics Camps for Youth
Principal Investigator(s):
Research Areas: Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Advancing knowledge of informal learning experiences that build adolescents' motivation for participation in STEM courses and careers, with a specific focus on introducing middle school African American and Latinx youth to the world of sports data analytics through events and summer camps.
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.

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