Research Projects
Filtered by: Human-Computer Interaction
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.
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.
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.
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
This $4.6M TRACE RERC grant advances research and development to expand accessible information and communication technologies for people with disabilities.
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
This $4.6M TRACE RERC grant advances research and development to expand accessible information and communication technologies for people with disabilities.
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.
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: National Science Foundation
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: National Science Foundation
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.
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
The Maryland Initiative for Digital Accessibility (MIDA) unites UMD researchers, educators, and partners to design “born-accessible” technologies, foster community engagement, and advance digital inclusion.
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
The Maryland Initiative for Digital Accessibility (MIDA) unites UMD researchers, educators, and partners to design “born-accessible” technologies, foster community engagement, and advance digital inclusion.
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.
Piloting Lab Discourse Graphs for Sustainable Research Communication
Principal Investigator(s): Joel Chan
Funders: The Navigation Fund; Chan Zuckerberg Initiative (CZI) Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project develops tooling for Discourse Graphs, a system enabling researchers to share and build on evidence-based research. Supported by $1.35M from the Chan-Zuckerberg Initiative and Navigation Fund, it collaborates with a PI at UW to enhance research workflows and open-science practices.
Principal Investigator(s): Joel Chan
Funders: The Navigation Fund; Chan Zuckerberg Initiative (CZI) Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project develops tooling for Discourse Graphs, a system enabling researchers to share and build on evidence-based research. Supported by $1.35M from the Chan-Zuckerberg Initiative and Navigation Fund, it collaborates with a PI at UW to enhance research workflows and open-science practices.
Postdoctoral Fellowship: STEMEdIPRF: SAGE4ICE: Student Analogy Generation Empowerment for Computing Education
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Youth Experience, Learning, and Digital Practices
This project develops classroom activities, digital scaffolding tools, and an online library to guide students in creating effective analogies for learning computing concepts. By improving comprehension and persistence in introductory courses, the project aims to broaden participation and strengthen the pipeline of future computing professionals.
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Youth Experience, Learning, and Digital Practices
This project develops classroom activities, digital scaffolding tools, and an online library to guide students in creating effective analogies for learning computing concepts. By improving comprehension and persistence in introductory courses, the project aims to broaden participation and strengthen the pipeline of future computing professionals.
Quantum Choreobotics: Democratizing Quantum Computing Through Interactive Dance/ Theater Performance, With On-Body Robots
Principal Investigator(s): Bill Kules
Funders: UMD Funded
Research Areas: Data Science, Analytics, and Visualization Digital Humanities Health Informatics Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
UMD researchers Bill Kules and Huaishu Peng are exploring quantum choreobotics, an interactive dance-theater performance where audiences influence robot movements to engage with quantum technology concepts. The project uses art and performance to make complex scientific ideas accessible and thought-provoking for the public.
Principal Investigator(s): Bill Kules
Funders: UMD Funded
Research Areas: Data Science, Analytics, and Visualization Digital Humanities Health Informatics Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
UMD researchers Bill Kules and Huaishu Peng are exploring quantum choreobotics, an interactive dance-theater performance where audiences influence robot movements to engage with quantum technology concepts. The project uses art and performance to make complex scientific ideas accessible and thought-provoking for the public.