Inactive Research Projects
The POD-Vis Cloud-Based Platform Generates Actionable Insights from Complex Clinical Datasets
Principal Investigator(s):
Funder: University of Maryland Baltimore Other Non-Federal
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Health Informatics
Principal Investigator(s):
Funder: University of Maryland Baltimore Other Non-Federal
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Health Informatics
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.
The Role of Emotions in Sociopolitical Polish and Lithuanian Social Media
Principal Investigator(s): Susannah Paletz
Research Areas: Social Networks, Online Communities, and Social Media
This project, run through ARLIS, investigates how emotion impacts information propagation and sharing in Polish and Lithuanian social media. Teams of in-country annotators independently assess each post, including all multimedia content, for over 22 distinct emotions inclusively on 0 to 100 scales--meaning that the same post will have different scores for different emotions. We will conduct multi-level statistical analyses of the impact of emotions, specific topics, and account and message features on social media sharing and engagement.
Principal Investigator(s): Susannah Paletz
Research Areas: Social Networks, Online Communities, and Social Media
This project, run through ARLIS, investigates how emotion impacts information propagation and sharing in Polish and Lithuanian social media. Teams of in-country annotators independently assess each post, including all multimedia content, for over 22 distinct emotions inclusively on 0 to 100 scales--meaning that the same post will have different scores for different emotions. We will conduct multi-level statistical analyses of the impact of emotions, specific topics, and account and message features on social media sharing and engagement.
Theory of Change Through Data
Principal Investigator(s): Wayne G. Lutters
Funder: New York University Other Non-Federal
Research Areas: Future of Work > Human-Computer Interaction > Library and Information Science
The Democratizing Data project supports federal agencies in understanding how their data assets are being used. Novel machine learning algorithms interrogate over 90 million publications to identify data usage, which is presented via a search and discovery platform with three interaction styles: interactive dashboards, Jupyter Notebooks, or direct API.
Principal Investigator(s): Wayne G. Lutters
Funder: New York University Other Non-Federal
Research Areas: Future of Work > Human-Computer Interaction > Library and Information Science
The Democratizing Data project supports federal agencies in understanding how their data assets are being used. Novel machine learning algorithms interrogate over 90 million publications to identify data usage, which is presented via a search and discovery platform with three interaction styles: interactive dashboards, Jupyter Notebooks, or direct API.
UMD Break Through Tech
Principal Investigator(s): Katherine Izsak
Funder: Cornell University Other Non-Federal
Research Areas: Future of Work
Break Through Tech works at the intersection of academia and industry to propel more women and underrepresented communities into technology education and careers. Break Through Techs' goal is to achieve gender equality in tech.
Principal Investigator(s): Katherine Izsak
Funder: Cornell University Other Non-Federal
Research Areas: Future of Work
Break Through Tech works at the intersection of academia and industry to propel more women and underrepresented communities into technology education and careers. Break Through Techs' goal is to achieve gender equality in tech.
When Does Encouraging Diverse Initial Solutions Lead to Better Final Solutions?
Principal Investigator(s): Joel Chan
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization
Designing high-performing engineering systems--for example, fuel-efficient aircraft, medical devices, new manufacturing and agricultural equipment--requires searching for high-quality solutions among many possible options.
Principal Investigator(s): Joel Chan
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization
Designing high-performing engineering systems--for example, fuel-efficient aircraft, medical devices, new manufacturing and agricultural equipment--requires searching for high-quality solutions among many possible options.
WIN: a Window Into Neuroregulation
Principal Investigator(s): Richard Marciano Greg Jansen
Funder: National Science Foundation
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Creating technology and best practices for conducting science that is situated in the classroom setting with the purpose of better understanding children's ability to self-regulate when presented with challenges, which seem to be ever-increasing in our digital era society.
Principal Investigator(s): Richard Marciano Greg Jansen
Funder: National Science Foundation
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Creating technology and best practices for conducting science that is situated in the classroom setting with the purpose of better understanding children's ability to self-regulate when presented with challenges, which seem to be ever-increasing in our digital era society.