Research Projects
Filtered by: Machine Learning, AI, Computational Linguistics, and Information Retrieval
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 Other Non-Federal
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 Other Non-Federal
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.
DASS: Learning Code(s): Community-Centered Design of Automated Content Moderation
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
Designing a Computer Science Pre-service Teacher Methods Course for Maryland
Principal Investigator(s): David Weintrop
Funders: State of MD
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
Principal Investigator(s): David Weintrop
Funders: State of MD
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
Designing AI-powered DIY Communication Tools with AAC users
Principal Investigator(s): Stephanie Valencia²
Funders: Corporation
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Principal Investigator(s): Stephanie Valencia²
Funders: Corporation
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
Detecting and Mapping War-induced Damage to Agricultural Fields in Ukraine using Multi-Modal Remote Sensing Data
Principal Investigator(s): Sergii Skakun
Funders: NASA - Proposal Only 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
Principal Investigator(s): Sergii Skakun
Funders: NASA - Proposal Only 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
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
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
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.
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.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.