Research Projects
Filtered by: Machine Learning, AI, Computational Linguistics, and Information Retrieval
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.
Professor of the Practice
Principal Investigator(s): Jason R. Baron Douglas W. Oard
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Memorandum of Understanding With MITRE Corporation. Collaborative research on methods of artificial intelligence used to improve administration of the Freedom of Information Act.
Principal Investigator(s): Jason R. Baron Douglas W. Oard
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Memorandum of Understanding With MITRE Corporation. Collaborative research on methods of artificial intelligence used to improve administration of the Freedom of Information Act.
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.
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.