Research Projects
Developing and Investigating Data Science Interventions Connected to University Athletics to Address Systemic Racism in Undergraduate STEM Education (better known as DataGOAT)
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
Digital Curation Fellows Program at the National Agricultural Library 2021-2026
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
The Digital Curation Fellows program is a partnership with the National Agricultural Library (NAL) to provide students from across all iSchool programs with research and practical experience solving real-world digital curation challenges. Digital curation fellows have contributed to numerous initiatives during this program’s several-year history, such as developing digital preservation plans, researching user experience, evaluating metadata quality, assessing diversity and equity of representation in digital collections, building new digital archives, and creating data analytics dashboards.
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
The Digital Curation Fellows program is a partnership with the National Agricultural Library (NAL) to provide students from across all iSchool programs with research and practical experience solving real-world digital curation challenges. Digital curation fellows have contributed to numerous initiatives during this program’s several-year history, such as developing digital preservation plans, researching user experience, evaluating metadata quality, assessing diversity and equity of representation in digital collections, building new digital archives, and creating data analytics dashboards.
E-VERIFY: For the Human-Machine Teaming for Intelligence Surveillance and Reconnaissance ISR) Analysis (HMT-ISR) Basic IDIQ
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction
A research endeavor with the College of Information at the University of Maryland, in partnership with the CMNS-Institute for Advanced Computer Studies and funded by Parallax Advanced Research.
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction
A research endeavor with the College of Information at the University of Maryland, in partnership with the CMNS-Institute for Advanced Computer Studies and funded by Parallax Advanced Research.
E-VERIFY: Task Order 002: Mission Analytics Technology and Research for Innovative eXploitation (MATRIX)
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Future of Work Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project focuses on developing mathematical and computational methods to advance machine learning and artificial intelligence, with applications that support U.S. Air Force, U.S. Space Force, and Department of Defense personnel.
Principal Investigator(s): Cody Buntain
Funders: Air Force Research Laboratory - Directorates Other Federal
Research Areas: Data Science, Analytics, and Visualization Future of Work Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project focuses on developing mathematical and computational methods to advance machine learning and artificial intelligence, with applications that support U.S. Air Force, U.S. Space Force, and Department of Defense personnel.
Enhancing Performance and Communication for Distributed Teams During Lunar Spacewalks
Principal Investigator(s): Susannah Paletz
Funders: NASA - Johnson Space Center Other Federal
Research Areas: Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This NASA-funded project studies how mission control teams supervise astronauts during spacewalks, aiming to improve communication, manage risks, and enhance multi-team performance during Artemis EVAs. It will develop and validate countermeasures to address delays, cognitive demands, and distributed team challenges.
Principal Investigator(s): Susannah Paletz
Funders: NASA - Johnson Space Center Other Federal
Research Areas: Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This NASA-funded project studies how mission control teams supervise astronauts during spacewalks, aiming to improve communication, manage risks, and enhance multi-team performance during Artemis EVAs. It will develop and validate countermeasures to address delays, cognitive demands, and distributed team challenges.
FAI: Advancing Deep Learning Towards Spatial Fairness
Principal Investigator(s): Sergii Skakun
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project aims to address spatial biases in AI, ensuring spatial fairness in real-world applications like agriculture and disaster management. Traditional machine learning struggles with spatial fairness due to data variations. The project proposes new statistical formulations, network architectures, fairness-driven adversarial learning, and a knowledge-enhanced approach for improved spatial dataset analysis. The results will integrate into geospatial software.fference between habits and behaviors ef
Principal Investigator(s): Sergii Skakun
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Machine Learning, AI, Computational Linguistics, and Information Retrieval
The project aims to address spatial biases in AI, ensuring spatial fairness in real-world applications like agriculture and disaster management. Traditional machine learning struggles with spatial fairness due to data variations. The project proposes new statistical formulations, network architectures, fairness-driven adversarial learning, and a knowledge-enhanced approach for improved spatial dataset analysis. The results will integrate into geospatial software.fference between habits and behaviors ef
From Echo Chambers to Empowerment: Social Media Narratives & School Safety Realities (#3517)
Principal Investigator(s): Celia Chen
Funders: UMD Funded
This project examines the effectiveness and community impact of school safety technologies by analyzing social media discourse and partnering with Prince George’s County public schools. Using mixed methods, including computational text analysis and participatory research, it centers community experiences to inform evidence-based strategies for preventing school shootings and promoting safe learning environments.
Principal Investigator(s): Celia Chen
Funders: UMD Funded
This project examines the effectiveness and community impact of school safety technologies by analyzing social media discourse and partnering with Prince George’s County public schools. Using mixed methods, including computational text analysis and participatory research, it centers community experiences to inform evidence-based strategies for preventing school shootings and promoting safe learning environments.
Future of Interface and Accessibility Workshop
Principal Investigator(s): Gregg Vanderheiden
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design
This project is focused on looking at the past and future of interface and accessibility including the development of a 20 year R&D agenda
Principal Investigator(s): Gregg Vanderheiden
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design
This project is focused on looking at the past and future of interface and accessibility including the development of a 20 year R&D agenda
Harnessing Generative AI to Support Exploration and Discovery in Library and Archival Collection
Principal Investigator(s): Richard Marciano
Funders: 8/1/2024 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Harnessing generative AI to support exploration and discovery in library and archival collections.
Principal Investigator(s): Richard Marciano
Funders: 8/1/2024 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Harnessing generative AI to support exploration and discovery in library and archival collections.
HCC: Small: The Incel Phenomenon: Assessing Radicalization and Deradicalization Online
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, led by Jennifer Golbeck at UMD’s College of Information, studies how radicalization and deradicalization occur within online incel communities.
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, led by Jennifer Golbeck at UMD’s College of Information, studies how radicalization and deradicalization occur within online incel communities.
Human-Like Coaching for Home PT Exercises
Principal Investigator(s): Galina Madjaroff Reitz
Funders: Maryland Industrial Partnerships UMD Funded
Research Areas: Health Informatics Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Researchers are developing an AI-powered physical therapy coach that uses real-time motion tracking and personalized feedback to improve exercise adherence and outcomes. By simulating human-like interaction and emotional engagement, the project aims to make home-based rehabilitation more effective and accessible.
Principal Investigator(s): Galina Madjaroff Reitz
Funders: Maryland Industrial Partnerships UMD Funded
Research Areas: Health Informatics Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Researchers are developing an AI-powered physical therapy coach that uses real-time motion tracking and personalized feedback to improve exercise adherence and outcomes. By simulating human-like interaction and emotional engagement, the project aims to make home-based rehabilitation more effective and accessible.
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
Funders: 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.
Principal Investigator(s): Vanessa Frias-Martinez
Funders: 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.