Research Projects
Inclusive ICT Rehabilitation Engineering Research Center (TRACE RERC)
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: Health and Human Services
Research Areas: Accessibility and Inclusive Design Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: Health and Human Services
Research Areas: Accessibility and Inclusive Design Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction
Information Technology Access RERC
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: DHHS-Administration for Community Living Other Federal
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
The University of Maryland Information Technology RERC aims to improve accessibility for people with disabilities through research, technology development, and standards creation. Key initiatives include individualizing generative AI, enhancing usability for older adults, developing cross-disability solutions, and creating open-source tools and guidelines to ensure broad, equitable access to information and communication technologies.
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: DHHS-Administration for Community Living Other Federal
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
The University of Maryland Information Technology RERC aims to improve accessibility for people with disabilities through research, technology development, and standards creation. Key initiatives include individualizing generative AI, enhancing usability for older adults, developing cross-disability solutions, and creating open-source tools and guidelines to ensure broad, equitable access to information and communication technologies.
Institute for Trustworthy AI in Law and Society (TRAILS)
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval
The TRAILS (Trustworthy AI in Law and Society) Institute, a collaboration among several universities, aims to enhance trust in AI systems. It focuses on community participation, transparent design, and best practices. Four key research thrusts address social values, technical design, socio-technical perceptions, and governance. The institute seeks to include historically marginalized communities and promote informed AI adoption.
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval
The TRAILS (Trustworthy AI in Law and Society) Institute, a collaboration among several universities, aims to enhance trust in AI systems. It focuses on community participation, transparent design, and best practices. Four key research thrusts address social values, technical design, socio-technical perceptions, and governance. The institute seeks to include historically marginalized communities and promote informed AI adoption.
Integration of Computer-Assisted Methods and Human Interactions to Understand Lesson Plan Quality and Teaching to Advance Middle-Grade Mathematics Instruction
Principal Investigator(s): Wei Ai
Funders: University of Washington Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This NSF-funded project uses machine learning, human coding, and teacher input to evaluate the quality of middle-grades mathematics lesson plans. By combining computational analysis with educator perspectives, it aims to improve how instructional materials are assessed and used in classrooms.
Principal Investigator(s): Wei Ai
Funders: University of Washington Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This NSF-funded project uses machine learning, human coding, and teacher input to evaluate the quality of middle-grades mathematics lesson plans. By combining computational analysis with educator perspectives, it aims to improve how instructional materials are assessed and used in classrooms.
Investigating the Information Practices of COVID Long-Haulers
Principal Investigator(s): Beth St. Jean Twanna Hodge Jane Behre J. Nicole Miller Miranda Downey
Funders: State of MD
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
Principal Investigator(s): Beth St. Jean Twanna Hodge Jane Behre J. Nicole Miller Miranda Downey
Funders: State of MD
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
Funders: 8/1/2022 – 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
Creating a national community focused on developing digital expertise and leadership skills among archival and library educators.
Principal Investigator(s): Richard Marciano
Funders: 8/1/2022 – 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
Creating a 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
Funders: 8/1/2021 – 7/31/2024 Institute of Museum and Library Services
Research Areas: Information Justice, Human Rights, and Technology Ethics Library and Information Science
This project aims to advance library and information science knowledge of immigrant well-being and increase capacity for libraries to serve as trusted spaces.
Principal Investigator(s): Ana Ndumu
Funders: 8/1/2021 – 7/31/2024 Institute of Museum and Library Services
Research Areas: Information Justice, Human Rights, and Technology Ethics Library and Information Science
This project aims to advance library and information science knowledge of immigrant well-being and increase capacity for libraries to serve as trusted spaces.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Funders: Unfunded
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.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
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.
Maryland Institute for Digital Accessibility (MIDA)
Principal Investigator(s): Jonathan Lazar Paul T. Jaeger J. Bern Jordan Galina Madjaroff Reitz Katherine Izsak
Funders: State of MD
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics
Principal Investigator(s): Jonathan Lazar Paul T. Jaeger J. Bern Jordan Galina Madjaroff Reitz Katherine Izsak
Funders: State of MD
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Funders: Unfunded
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.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
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.
Mitigating online COVID misinformation costs: From individual to field interventions
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
NSF Convergence Accelerator Track J: NourishNet – A Food Recovery Toolbox
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Smart Cities and Connected Communities
NourishNet is developing tools to fight food insecurity and waste, including FoodLoops, a platform for surplus food distribution, and Quantum Nose, a sensor that detects early food spoilage. By combining real-time data, consumer education, and stakeholder collaboration, the project strengthens food system resiliency and promotes equitable access to healthy food.
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Smart Cities and Connected Communities
NourishNet is developing tools to fight food insecurity and waste, including FoodLoops, a platform for surplus food distribution, and Quantum Nose, a sensor that detects early food spoilage. By combining real-time data, consumer education, and stakeholder collaboration, the project strengthens food system resiliency and promotes equitable access to healthy food.