Research Projects
Filtered by: Data Science, Analytics, and Visualization
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
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
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.
Maryland Sports Data Analytics Camps for Youth
Principal Investigator(s):
Research Areas: Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Advancing knowledge of informal learning experiences that build adolescents' motivation for participation in STEM courses and careers, with a specific focus on introducing middle school African American and Latinx youth to the world of sports data analytics through events and summer camps.
Principal Investigator(s):
Research Areas: Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Advancing knowledge of informal learning experiences that build adolescents' motivation for participation in STEM courses and careers, with a specific focus on introducing middle school African American and Latinx youth to the world of sports data analytics through events and summer camps.
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
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
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
Funder: Social Science Research Council Other
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
Funder: Social Science Research Council Other
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: MidAtlantic Food Resiliency Network – Securing the Future of Food through a Multi-Mindset Approach
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The Mid-Atlantic Food Resiliency Network (MFRN) aims to improve food security in the Mid-Atlantic, starting in Prince George’s County. Collaborating across multiple disciplines, the MFRN will develop tools and systems to reduce hunger, waste, and food deserts. Initiatives include understanding food behaviors, repurposing food waste, and training future food security leaders.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The Mid-Atlantic Food Resiliency Network (MFRN) aims to improve food security in the Mid-Atlantic, starting in Prince George’s County. Collaborating across multiple disciplines, the MFRN will develop tools and systems to reduce hunger, waste, and food deserts. Initiatives include understanding food behaviors, repurposing food waste, and training future food security leaders.
Piloting an Online National Collaborative Network for Integrating Computational Thinking into Library and Archival Education and Practice
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Piloting an online national collaborative network of educators and practitioners to enable the sharing and dissemination of computational case studies and lesson plans through an open source, cloud-based interactive platform based on Jupyter Notebooks.
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Piloting an online national collaborative network of educators and practitioners to enable the sharing and dissemination of computational case studies and lesson plans through an open source, cloud-based interactive platform based on Jupyter Notebooks.
PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling During Pandemics (PandEval)
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The PandEval project aims to enhance pandemic response by utilizing diverse data sources, including social media insights and real-life behavior tracking. It seeks to improve public health messaging and localized policies, with customized epidemiological models. The project's innovation lies in creating a Pand-Index, aiding individual decisions on measures like social distancing.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The PandEval project aims to enhance pandemic response by utilizing diverse data sources, including social media insights and real-life behavior tracking. It seeks to improve public health messaging and localized policies, with customized epidemiological models. The project's innovation lies in creating a Pand-Index, aiding individual decisions on measures like social distancing.
SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait
Principal Investigator(s): Naeemul Hassan
Funder: 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
Funder: 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: EDU: Collaborative: Connecting Contexts: Building Foundational Digital Privacy and Security Skills for Elementary School Children, Teachers, and Parents
Principal Investigator(s): Jessica Vitak Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization > Human-Computer Interaction
Promoting elementary school children's privacy/cybersecurity learning across the two contexts where they spend most of their time, home and school, through the creation of curriculum and related educational materials tailored to grade level.
Principal Investigator(s): Jessica Vitak Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization > Human-Computer Interaction
Promoting elementary school children's privacy/cybersecurity learning across the two contexts where they spend most of their time, home and school, through the creation of curriculum and related educational materials tailored to grade level.
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
Funder: 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
Funder: 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.
Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES)
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
The PROMISE Academy
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization
Providing new college students with the necessary tools for success through intensive first level developmental courses, tutoring, advising, and the creation of learning communities comprised of faculty, staff, tutors, and advisors.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization
Providing new college students with the necessary tools for success through intensive first level developmental courses, tutoring, advising, and the creation of learning communities comprised of faculty, staff, tutors, and advisors.