Research Projects
Designing a Computer Science Pre-service Teacher Methods Course for Maryland
Principal Investigator(s): David Weintrop
Funder: University System of Maryland Other
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
Principal Investigator(s): David Weintrop
Funder: University System of Maryland Other
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
Designing Equitable Computational Thinking Learning Opportunities in Under-Resourced Elementary Mathematics Classrooms
Principal Investigator(s): David Weintrop
Funder: The National Academy of Education/Spencer Postdoctoral Fellowship Program Other
Research Areas: Youth Experience, Learning, and Digital Practices
Integrating computational thinking into elementary mathematics classrooms in a way that empowers learners to draw on their Funds of Knowledge while also working within the constraints of the public education system.
Principal Investigator(s): David Weintrop
Funder: The National Academy of Education/Spencer Postdoctoral Fellowship Program Other
Research Areas: Youth Experience, Learning, and Digital Practices
Integrating computational thinking into elementary mathematics classrooms in a way that empowers learners to draw on their Funds of Knowledge while also working within the constraints of the public education system.
Developing a Digital Asset Management System for the Archival Holdings of the Mary McLeod Bethune Council House National Historic Site
Principal Investigator(s): Richard Marciano
Funder: USDOI National Park Service
Research Areas: Archival Science > Digital Humanities > Information Justice, Human Rights, and Technology Ethics
Creating a cutting-edge Digital Asset Management System with the National Park Service (NPS) to preserve and manage the digital assets of the Mary McLeod Bethune Council House National Historic Site.
Principal Investigator(s): Richard Marciano
Funder: USDOI National Park Service
Research Areas: Archival Science > Digital Humanities > Information Justice, Human Rights, and Technology Ethics
Creating a cutting-edge Digital Asset Management System with the National Park Service (NPS) to preserve and manage the digital assets of the Mary McLeod Bethune Council House National Historic Site.
Digital Curation Fellows Program – National Agricultural Library
Principal Investigator(s): Katrina Fenlon
Funder: USDA Agricultural Research Service
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
Funder: USDA Agricultural Research Service
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: Modernizing NCSES Data Collection Approaches (Year 2)
Principal Investigator(s):
Funder: University of Michigan Other
Principal Investigator(s):
Funder: University of Michigan Other
Earth Observation for National Agricultural Monitoring
Principal Investigator(s): Sergii Skakun
Funder: NASA Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project aims to advance national agriculture monitoring with Earth Observations (EO) data in East and Southern Africa using machine learning tools and open source data to develop baseline datasets.
Principal Investigator(s): Sergii Skakun
Funder: NASA Other
Research Areas: Future of Work > Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project aims to advance national agriculture monitoring with Earth Observations (EO) data in East and Southern Africa using machine learning tools and open source data to develop baseline datasets.
Environmental Injustice and Deaths of Despair: Lessons from Montana’s Tribal Lands
Principal Investigator(s):
Research Areas: Health Informatics > Information Justice, Human Rights, and Technology Ethics
The proposed project uses the case of Native American Lands in Montana to investigate the dynamic interactions between environmental change and socio-economic conditions, in order to identify potential pathways whereby environmental hardship may contribute to (and result from) forms of socio-economic distress linked to deaths of despair.
Principal Investigator(s):
Research Areas: Health Informatics > Information Justice, Human Rights, and Technology Ethics
The proposed project uses the case of Native American Lands in Montana to investigate the dynamic interactions between environmental change and socio-economic conditions, in order to identify potential pathways whereby environmental hardship may contribute to (and result from) forms of socio-economic distress linked to deaths of despair.
FAI: Advancing Deep Learning Towards Spatial Fairness
Principal Investigator(s): Sergii Skakun
Funder: University of Pittsburgh 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
Funder: University of Pittsburgh 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
Funder: 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
Funder: 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
Heal Us: Reimagining and co-developing curricula for maternal health professionals
Principal Investigator(s): Amelia Gibson
Funder: University of North Carolina at Chapel Hill Other
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Health Informatics > Information Justice, Human Rights, and Technology Ethics
BELIEVE (which stands For “Building Equitable Linkages With Interprofessional Education Valuing Everyone) is a multi-institutional project focused on developing and testing interprofessional curricular interventions for the purpose of reducing Black maternal mortality and morbidity and improving birth experiences in the United States.
Principal Investigator(s): Amelia Gibson
Funder: University of North Carolina at Chapel Hill Other
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Health Informatics > Information Justice, Human Rights, and Technology Ethics
BELIEVE (which stands For “Building Equitable Linkages With Interprofessional Education Valuing Everyone) is a multi-institutional project focused on developing and testing interprofessional curricular interventions for the purpose of reducing Black maternal mortality and morbidity and improving birth experiences in the United States.
Human-Agent Teaming on Intelligence Tasks
Principal Investigator(s): Susannah Paletz
Funder: US Army Research Office
Research Areas: Future of Work
Our goal is to create a platform for running experiments that would simulate an AI intervention into intelligence analysis tasks, specifically involving a human shift handover. Participants would work through materials, including notes and feedback from the “previous” analyst, to solve a fictional intelligence task. This study examines how potential AIs can influence team cognition and decision making.
Principal Investigator(s): Susannah Paletz
Funder: US Army Research Office
Research Areas: Future of Work
Our goal is to create a platform for running experiments that would simulate an AI intervention into intelligence analysis tasks, specifically involving a human shift handover. Participants would work through materials, including notes and feedback from the “previous” analyst, to solve a fictional intelligence task. This study examines how potential AIs can influence team cognition and decision making.
IARPA BETTER: Multilingual Fine-grained Decompositional Analysis
Principal Investigator(s):
Funder: USODNI Intelligence Advanced Research Projects Activity
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Developing enhanced methods for personalized, multilingual semantic extraction and retrieval from text, in support of IARPA's goal of providing users with a system that quickly and accurately extracts complex semantic information, targeted for a specific user, from text.
Principal Investigator(s):
Funder: USODNI Intelligence Advanced Research Projects Activity
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Developing enhanced methods for personalized, multilingual semantic extraction and retrieval from text, in support of IARPA's goal of providing users with a system that quickly and accurately extracts complex semantic information, targeted for a specific user, from text.