Research Projects
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.
Read, Watch, Play: Development of a Pedagogical Approach and Technical Infrastructure to Build Gamification, Game-Based Learning, and Other Hands-On Projects into Courses across Campus
Principal Investigator(s): Caro Williams-Pierce Andrew Fellows Katherine Izsak Pamela Duffy
Funders: State of MD
Research Areas: Youth Experience, Learning, and Digital Practices
This Teaching Innovation Grant project at UMD develops a digital platform and pedagogy to help faculty integrate play, gamification, and multimodal resources into teaching.
Principal Investigator(s): Caro Williams-Pierce Andrew Fellows Katherine Izsak Pamela Duffy
Funders: State of MD
Research Areas: Youth Experience, Learning, and Digital Practices
This Teaching Innovation Grant project at UMD develops a digital platform and pedagogy to help faculty integrate play, gamification, and multimodal resources into teaching.
Ready Now: Supporting Youth and Families During Crisis
Principal Investigator(s): Mega Subramaniam Nitzan Koren
Funders: 8/1/2023 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Library and Information Science
This project aims to broaden and improve the efficacy of the publication: Library Staff as Public Servants: A Field Guide for Preparing to Support Communities in Crisis.
Principal Investigator(s): Mega Subramaniam Nitzan Koren
Funders: 8/1/2023 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Library and Information Science
This project aims to broaden and improve the efficacy of the publication: Library Staff as Public Servants: A Field Guide for Preparing to Support Communities in Crisis.
Reducing the gender gap in AfD discussions: an evidence scoring approach
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Wikimedia Foundation Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project examines how Wikipedia’s Articles for Deletion (AfD) discussions influence the gender gap by analyzing biases in notability assessments and use of external evidence.
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Wikimedia Foundation Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project examines how Wikipedia’s Articles for Deletion (AfD) discussions influence the gender gap by analyzing biases in notability assessments and use of external evidence.
Revolutionizing Space-Based ISR through Decentralized Systems & In-Orbit ML Computing for Near-Real-Time Intelligence
Principal Investigator(s): Sergii Skakun
Funders: Little Place Labs Other Non-Federal
This project, funded through a U.S. Department of the Air Force research contract with Little Place Labs, develops edge-computing applications for satellites to analyze data in orbit. By enabling real-time detection of maritime activities, vessel classification, and other security-relevant events, the research aims to enhance intelligence, surveillance, and reconnaissance capabilities while reducing latency and security vulnerabilities in space-based systems.
Principal Investigator(s): Sergii Skakun
Funders: Little Place Labs Other Non-Federal
This project, funded through a U.S. Department of the Air Force research contract with Little Place Labs, develops edge-computing applications for satellites to analyze data in orbit. By enabling real-time detection of maritime activities, vessel classification, and other security-relevant events, the research aims to enhance intelligence, surveillance, and reconnaissance capabilities while reducing latency and security vulnerabilities in space-based systems.
SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait
Principal Investigator(s): Naeemul Hassan
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
Developing novel techniques - through the application of state-of-the-art machine learning - to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems.
Principal Investigator(s): Naeemul Hassan
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
Developing novel techniques - through the application of state-of-the-art machine learning - to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems.
SaTC: CORE: Medium: Learning Code(s): Community-Centered Design of Automated Content Moderation
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This research project aims to improve online community moderation by using machine learning and natural language processing. It focuses on learning from existing community decisions, supporting moderators, and creating adaptable tools. The goal is healthier online spaces and better working conditions for moderators.
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This research project aims to improve online community moderation by using machine learning and natural language processing. It focuses on learning from existing community decisions, supporting moderators, and creating adaptable tools. The goal is healthier online spaces and better working conditions for moderators.
SCC-IRG Track 1: Inclusive Public Transit Toolkit to Assess Quality of Service Across Socioeconomic Status in Baltimore City
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.