Research Projects
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: Parallax Advanced Research Other Non-Federal
Principal Investigator(s): Cody Buntain
Funders: Parallax Advanced Research Other Non-Federal
E-VERIFY: Task Order 002: Mission Analytics Technology and Research for Innovative eXploitation (MATRIX)
Principal Investigator(s): Cody Buntain
Funders: Parallax Advanced Research Other Federal
Principal Investigator(s): Cody Buntain
Funders: Parallax Advanced Research Other Federal
Enhancing Performance and Communication for Distributed Teams During Lunar Spacewalks
Principal Investigator(s): Susannah Paletz
Funders: NASA Other Federal
Research Areas: Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Principal Investigator(s): Susannah Paletz
Funders: NASA Other Federal
Research Areas: Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Ethical VR/AR/XR Research Design with Youth and Families
Principal Investigator(s): beth bonsignore Tamara Clegg
Funders: University of Iowa Other Non-Federal
Principal Investigator(s): beth bonsignore Tamara Clegg
Funders: University of Iowa Other Non-Federal
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
From Echo Chambers to Empowerment: Social Media Narratives & School Safety Realities (#3517)
Principal Investigator(s): Celia Chen
Funders: UMD Funded
Principal Investigator(s): Celia Chen
Funders: UMD Funded
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: 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: 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
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.