Research Projects
Filtered by: Data Science, Analytics, and Visualization
Additive Manufacturing Digital Curation and Data Management
Principal Investigator(s): Richard Marciano
Funder: US Army Research Office: Army Research Lab (ARL) Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization
Exploring digital curation, data management, data mining, and the development of a digital asset management system for Additive Manufacturing
Principal Investigator(s): Richard Marciano
Funder: US Army Research Office: Army Research Lab (ARL) Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization
Exploring digital curation, data management, data mining, and the development of a digital asset management system for Additive Manufacturing
An Assessment of Pretrial Risk across Maryland Jurisdictions using Client Legal Utility Enging (CLUE) Data
Principal Investigator(s): Zubin Jelveh
Funder: STMD-Governor's Office of Crime Control & Prevention Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
With funding from the Maryland Governor's Office of Crime Control and Prevention, the project aims to understand why pretrial detention decisions are made and whether they align with the risk posed by defendants. By analyzing a large dataset of criminal cases, the team will investigate the predictability of pretrial risk and the court's decision-making. The research will provide insights to improve policy and practice, reducing unnecessary detention while ensuring public safety.
Principal Investigator(s): Zubin Jelveh
Funder: STMD-Governor's Office of Crime Control & Prevention Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
With funding from the Maryland Governor's Office of Crime Control and Prevention, the project aims to understand why pretrial detention decisions are made and whether they align with the risk posed by defendants. By analyzing a large dataset of criminal cases, the team will investigate the predictability of pretrial risk and the court's decision-making. The research will provide insights to improve policy and practice, reducing unnecessary detention while ensuring public safety.
Building a sustainable future for anthropology’s archives: Researching primary source data lifecycles, infrastructures, and reuse
Principal Investigator(s): Diana E. Marsh Katrina Fenlon
Funder: National Science Foundation
Research Areas: Archival Science > Data Science, Analytics, and Visualization
This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs. It investigates barriers to data reusability and seeks sustainable ways to adapt linked data infrastructures. The research involves focus group discussions, open access platforms, training modules, and a virtual symposium to enhance the sharing of primary source cultural research data and support interdisciplinary collaboration in anthropology.
Principal Investigator(s): Diana E. Marsh Katrina Fenlon
Funder: National Science Foundation
Research Areas: Archival Science > Data Science, Analytics, and Visualization
This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs. It investigates barriers to data reusability and seeks sustainable ways to adapt linked data infrastructures. The research involves focus group discussions, open access platforms, training modules, and a virtual symposium to enhance the sharing of primary source cultural research data and support interdisciplinary collaboration in anthropology.
CHS: Medium: Collaborative Research: Teachable Activity Trackers for Older Adults
Principal Investigator(s): Eun Kyoung Choe
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Health Informatics > Human-Computer Interaction
Pushing the boundaries of how personal tracking devices, such as smart watches, can better support older adults---by identifying what health/activities data would be most useful for older adults if tracked, how to collect/track this data, and utilizing this information to develop a new personalized, multimodal activity tracker.
Principal Investigator(s): Eun Kyoung Choe
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Health Informatics > Human-Computer Interaction
Pushing the boundaries of how personal tracking devices, such as smart watches, can better support older adults---by identifying what health/activities data would be most useful for older adults if tracked, how to collect/track this data, and utilizing this information to develop a new personalized, multimodal activity tracker.
Computational Treatments to re-member the Legacy of Slavery (CT-LoS)
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Using Computational Archival Science to unlock records related to the Legacy of Slavery and provide new point of interaction and analysis.
Coupled Statistics-Physics Guided Learning to Harness Hetergeneous Earth Data at Large Scales
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Data Science, Analytics, and Visualization
This project is funded by NASA's Advanced Information Systems and Technology (AIST) program. It aims to advance machine learning for Earth Science problems. Specifically, we will develop new technologies that address two major challenges facing machine learning for broad Earth Science applications—spatial heterogeneity, where satellite observations and their relationships to the prediction targets vary over space, and the limited and highly localized nature of ground-truth data that are needed to train the algorithms.
Principal Investigator(s): Sergii Skakun
Funder: NASA - Goddard Space Flight Center Other
Research Areas: Data Science, Analytics, and Visualization
This project is funded by NASA's Advanced Information Systems and Technology (AIST) program. It aims to advance machine learning for Earth Science problems. Specifically, we will develop new technologies that address two major challenges facing machine learning for broad Earth Science applications—spatial heterogeneity, where satellite observations and their relationships to the prediction targets vary over space, and the limited and highly localized nature of ground-truth data that are needed to train the algorithms.
CRII: CHS: Investigating Multilingual Teams Communication and Collaborative Writing
Principal Investigator(s): Ge Gao
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Future of Work > Information Justice, Human Rights, and Technology Ethics > Youth Experience, Learning, and Digital Practices
This project investigates new ways to create grounding in multilingual teams engaged in collaborative writing. It will improve understanding and develop new tools.
Principal Investigator(s): Ge Gao
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Future of Work > Information Justice, Human Rights, and Technology Ethics > Youth Experience, Learning, and Digital Practices
This project investigates new ways to create grounding in multilingual teams engaged in collaborative writing. It will improve understanding and develop new tools.
Digital Curation Fellows Program – National Agricultural Library
Principal Investigator(s): Katrina Fenlon
Funder: USDA Agricultural Research Service
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The Digital Curation Fellows program is a partnership with the National Agricultural Library (NAL) to provide students from across all iSchool programs with research and practical experience solving real-world digital curation challenges. Digital curation fellows have contributed to numerous initiatives during this program’s several-year history, such as developing digital preservation plans, researching user experience, evaluating metadata quality, assessing diversity and equity of representation in digital collections, building new digital archives, and creating data analytics dashboards.
Principal Investigator(s): Katrina Fenlon
Funder: USDA Agricultural Research Service
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The Digital Curation Fellows program is a partnership with the National Agricultural Library (NAL) to provide students from across all iSchool programs with research and practical experience solving real-world digital curation challenges. Digital curation fellows have contributed to numerous initiatives during this program’s several-year history, such as developing digital preservation plans, researching user experience, evaluating metadata quality, assessing diversity and equity of representation in digital collections, building new digital archives, and creating data analytics dashboards.
FAI: Advancing Deep Learning Towards Spatial Fairness
Principal Investigator(s): Sergii Skakun
Funder: University of Pittsburgh 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
Funder: University of Pittsburgh 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
Funder: 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
Funder: 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.
Inclusive ICT RERC
Principal Investigator(s): Gregg Vanderheiden J. Bern Jordan Hernisa Kacorri Amanda Lazar Jonathan Lazar
Funder: HHS / ACL / National Institute on Disability, Independent Living, and Rehabilitation Research Other
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Human-Computer Interaction > Information Justice, Human Rights, and Technology Ethics
Ensuring that existing information and communication technologies (ICT) solutions for people with disabilities are known, effective, findable, more affordable, and available on every computer or digital technology platform; and exploring the emerging next-next-generation interface technologies for which there are no effective accessibility guidelines or standards, and problem-solving in advance of these technologies.
Principal Investigator(s): Gregg Vanderheiden J. Bern Jordan Hernisa Kacorri Amanda Lazar Jonathan Lazar
Funder: HHS / ACL / National Institute on Disability, Independent Living, and Rehabilitation Research Other
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Human-Computer Interaction > Information Justice, Human Rights, and Technology Ethics
Ensuring that existing information and communication technologies (ICT) solutions for people with disabilities are known, effective, findable, more affordable, and available on every computer or digital technology platform; and exploring the emerging next-next-generation interface technologies for which there are no effective accessibility guidelines or standards, and problem-solving in advance of these technologies.
Launching the TALENT Network to Promote the Training of Archival & Library Educators w. iNnovative Technologies
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The TALENT Network (Training of Archival & Library Educators with iNnovative Technologies) brings together experts from across the United States (including archivists, librarians, Library and Information Science educators, historians, learning scientists, cognitive scientists, computer scientists, and software engineers) in order to create a durable, diverse, and multidisciplinary national community focused on developing digital expertise and leadership skills among archival and library educators.
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services Other
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The TALENT Network (Training of Archival & Library Educators with iNnovative Technologies) brings together experts from across the United States (including archivists, librarians, Library and Information Science educators, historians, learning scientists, cognitive scientists, computer scientists, and software engineers) in order to create a durable, diverse, and multidisciplinary national community focused on developing digital expertise and leadership skills among archival and library educators.