Research Projects
Filtered by: Machine Learning, AI, Computational Linguistics, and Information Retrieval
An AI-Enhanced Colleague for Teachers: Developing and Studying an Innovative Platform for Efficient, Inclusive Middle-Grade Mathematics Lesson Planning
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project supports middle school math teachers by developing an AI-powered lesson planning tool that enhances efficiency, quality, and inclusivity. Integrating generative AI with research-based practices, it offers personalized guidance for creating effective lessons. The project also examines impacts on teacher stress, instructional effectiveness, and student learning outcomes.
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project supports middle school math teachers by developing an AI-powered lesson planning tool that enhances efficiency, quality, and inclusivity. Integrating generative AI with research-based practices, it offers personalized guidance for creating effective lessons. The project also examines impacts on teacher stress, instructional effectiveness, and student learning outcomes.
CAREER: Self-Directed Human-LLM Coordination for Language Learning and Information Seeking
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Collaborative Research: ER2: Developing Educational Resources for the Ethical Use of Pervasive Data
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Collaborative Research: Using Artificial Intelligence To Improve Administration of the Freedom of Information Act (FOIA)
Principal Investigator(s): Jason R. Baron Douglas W. Oard
Funders: Unfunded
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Memorandum of Understanding with MITRE Corporation to research the application of various forms of artificial intelligence (AI) including machine learning (ML) methods to aid in the redaction of documents corresponding to one or more FOIA exemptions.
Principal Investigator(s): Jason R. Baron Douglas W. Oard
Funders: Unfunded
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Memorandum of Understanding with MITRE Corporation to research the application of various forms of artificial intelligence (AI) including machine learning (ML) methods to aid in the redaction of documents corresponding to one or more FOIA exemptions.
Computational Thinking to Unlock the Japanese American WWII Camp Experience
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
Designing AI-powered DIY Communication Tools with AAC users
Principal Investigator(s): Stephanie Valencia²
Funders: Google Corporation
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This Google Research Scholar-funded project designs AI-powered DIY communication tools to enhance accessibility for augmentative and alternative communication (AAC) users.
Principal Investigator(s): Stephanie Valencia²
Funders: Google Corporation
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This Google Research Scholar-funded project designs AI-powered DIY communication tools to enhance accessibility for augmentative and alternative communication (AAC) users.
Detecting and Mapping War-induced Damage to Agricultural Fields in Ukraine using Multi-Modal Remote Sensing Data
Principal Investigator(s): Sergii Skakun
Funders: NASA Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Smart Cities and Connected Communities Social Networks, Online Communities, and Social Media
This project advances remote sensing methods to map war-induced damage to Ukraine’s agricultural fields using infrared and visible spectrum satellite data. By developing deep-learning and data fusion techniques, the research will detect artillery craters, burned areas, and abandoned fields to assess the war’s impact on agriculture at scale.
Principal Investigator(s): Sergii Skakun
Funders: NASA Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Smart Cities and Connected Communities Social Networks, Online Communities, and Social Media
This project advances remote sensing methods to map war-induced damage to Ukraine’s agricultural fields using infrared and visible spectrum satellite data. By developing deep-learning and data fusion techniques, the research will detect artillery craters, burned areas, and abandoned fields to assess the war’s impact on agriculture at scale.
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
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.
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.