Research Projects
FAI: A Human-centered Approach to Developing Accessible and Reliable Machine Translation
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
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
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
HCC: Small: The Incel Phenomenon: Assessing Radicalization and Deradicalization Online
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
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.
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 Non-Federal
Principal Investigator(s): J. Bern Jordan Amanda Lazar Hernisa Kacorri
Funders: DHHS-Administration for Community Living Other Non-Federal
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: Human-Computer Interaction
Principal Investigator(s): Wei Ai
Funders: University of Washington Other Non-Federal
Research Areas: Human-Computer Interaction
Inverting Colonial Archival Structures: Increasing Discovery and Access for Indigenous Communities through SNAC
Principal Investigator(s): Diana E. Marsh
Funders: Institute of Museum and Library Services
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.
Principal Investigator(s): Diana E. Marsh
Funders: Institute of Museum and Library Services
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
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
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: Institute of Museum and Library Services
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.
Principal Investigator(s): Richard Marciano
Funders: Institute of Museum and Library Services
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.