Research Projects
Filtered by: Data Science, Analytics, and Visualization
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.
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: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This NSF-funded project uses machine learning, human coding, and teacher input to evaluate the quality of middle-grades mathematics lesson plans. By combining computational analysis with educator perspectives, it aims to improve how instructional materials are assessed and used in classrooms.
Principal Investigator(s): Wei Ai
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This NSF-funded project uses machine learning, human coding, and teacher input to evaluate the quality of middle-grades mathematics lesson plans. By combining computational analysis with educator perspectives, it aims to improve how instructional materials are assessed and used in classrooms.
Launching the TALENT Network to Promote the Training of Archival & Library Educators w. iNnovative Technologies
Principal Investigator(s): Richard Marciano
Funders: 8/1/2022 – 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
Creating a national community focused on developing digital expertise and leadership skills among archival and library educators.
Principal Investigator(s): Richard Marciano
Funders: 8/1/2022 – 4/9/2025 Institute of Museum and Library Services
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science
Creating a national community focused on developing digital expertise and leadership skills among archival and library educators.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Funders: Unfunded
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
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.
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.
Mitigating online COVID misinformation costs: From individual to field interventions
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
NSF Convergence Accelerator Track J: NourishNet – A Food Recovery Toolbox
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Smart Cities and Connected Communities
NourishNet is developing tools to fight food insecurity and waste, including FoodLoops, a platform for surplus food distribution, and Quantum Nose, a sensor that detects early food spoilage. By combining real-time data, consumer education, and stakeholder collaboration, the project strengthens food system resiliency and promotes equitable access to healthy food.
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Smart Cities and Connected Communities
NourishNet is developing tools to fight food insecurity and waste, including FoodLoops, a platform for surplus food distribution, and Quantum Nose, a sensor that detects early food spoilage. By combining real-time data, consumer education, and stakeholder collaboration, the project strengthens food system resiliency and promotes equitable access to healthy food.
P3 (Pregnancy and Postpartum/Preconception) EQUATE (Enhancing Access and Quality to Achieve Equitable Maternal and Infant Health) Network
Principal Investigator(s): Jasmine Garland McKinney
Funders: American Heart Association Other Non-Federal
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Health Informatics Information Justice, Human Rights, and Technology Ethics
This project validates the Prepartum Form for Evaluating Race-Related Psychological Stressors (PP-FERRPS)©, a screening tool designed to measure how race-related stressors affect Black perinatal women’s mental health. By refining this tool, the study aims to address gaps in traditional assessments and improve clinical support in maternal care.
Principal Investigator(s): Jasmine Garland McKinney
Funders: American Heart Association Other Non-Federal
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Health Informatics Information Justice, Human Rights, and Technology Ethics
This project validates the Prepartum Form for Evaluating Race-Related Psychological Stressors (PP-FERRPS)©, a screening tool designed to measure how race-related stressors affect Black perinatal women’s mental health. By refining this tool, the study aims to address gaps in traditional assessments and improve clinical support in maternal care.
Piloting Lab Discourse Graphs for Sustainable Research Communication
Principal Investigator(s): Joel Chan
Funders: The Navigation Fund; Chan Zuckerberg Initiative (CZI) Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project develops tooling for Discourse Graphs, a system enabling researchers to share and build on evidence-based research. Supported by $1.35M from the Chan-Zuckerberg Initiative and Navigation Fund, it collaborates with a PI at UW to enhance research workflows and open-science practices.
Principal Investigator(s): Joel Chan
Funders: The Navigation Fund; Chan Zuckerberg Initiative (CZI) Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
This project develops tooling for Discourse Graphs, a system enabling researchers to share and build on evidence-based research. Supported by $1.35M from the Chan-Zuckerberg Initiative and Navigation Fund, it collaborates with a PI at UW to enhance research workflows and open-science practices.
Postdoctoral Fellowship: STEMEdIPRF: SAGE4ICE: Student Analogy Generation Empowerment for Computing Education
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Youth Experience, Learning, and Digital Practices
This project develops classroom activities, digital scaffolding tools, and an online library to guide students in creating effective analogies for learning computing concepts. By improving comprehension and persistence in introductory courses, the project aims to broaden participation and strengthen the pipeline of future computing professionals.
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Data Science, Analytics, and Visualization Future of Work Human-Computer Interaction Youth Experience, Learning, and Digital Practices
This project develops classroom activities, digital scaffolding tools, and an online library to guide students in creating effective analogies for learning computing concepts. By improving comprehension and persistence in introductory courses, the project aims to broaden participation and strengthen the pipeline of future computing professionals.
Quantum Choreobotics: Democratizing Quantum Computing Through Interactive Dance/ Theater Performance, With On-Body Robots
Principal Investigator(s): Bill Kules
Funders: UMD Funded
Research Areas: Data Science, Analytics, and Visualization Digital Humanities Health Informatics Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
UMD researchers Bill Kules and Huaishu Peng are exploring quantum choreobotics, an interactive dance-theater performance where audiences influence robot movements to engage with quantum technology concepts. The project uses art and performance to make complex scientific ideas accessible and thought-provoking for the public.
Principal Investigator(s): Bill Kules
Funders: UMD Funded
Research Areas: Data Science, Analytics, and Visualization Digital Humanities Health Informatics Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval
UMD researchers Bill Kules and Huaishu Peng are exploring quantum choreobotics, an interactive dance-theater performance where audiences influence robot movements to engage with quantum technology concepts. The project uses art and performance to make complex scientific ideas accessible and thought-provoking for the public.