Research Projects
Filtered by: National Science Foundation
Accessible Visualization for Blind Users
Principal Investigator(s): Jonathan Lazar
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design
This project aims to enhance accessibility to large-scale data analysis for blind and low-vision individuals, bridging the gap in current tools and technologies. It focuses on creating cost-effective, user-friendly data representations based on sound, touch, and physical computing. The research involves understanding user needs and designing practical accessible data applications in collaboration with the blind community.
Principal Investigator(s): Jonathan Lazar
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design
This project aims to enhance accessibility to large-scale data analysis for blind and low-vision individuals, bridging the gap in current tools and technologies. It focuses on creating cost-effective, user-friendly data representations based on sound, touch, and physical computing. The research involves understanding user needs and designing practical accessible data applications in collaboration with the blind community.
Building a sustainable future for anthropology’s archives: Researching primary source data lifecycles, infrastructures, and reuse
Principal Investigator(s): Diana E. Marsh Katrina Fenlon
Funders: 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This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs.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
Funders: 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This project aims to improve the preservation and accessibility of valuable, unpublished anthropological data, including field notebooks, recordings, and photographs.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.
CAREER: Advancing Remote Collaboration: Inclusive Design for People with Dementia
Principal Investigator(s): Amanda Lazar
Funders: National Science Foundation
Research Areas: Health Informatics Human-Computer Interaction Social Networks, Online Communities, and Social Media
Technology increasingly provides opportunities to interact remotely with others. People with cognitive impairment can be excluded from these opportunities when technology is not designed with their needs, preferences, and abilities in mind.
Principal Investigator(s): Amanda Lazar
Funders: National Science Foundation
Research Areas: Health Informatics Human-Computer Interaction Social Networks, Online Communities, and Social Media
Technology increasingly provides opportunities to interact remotely with others. People with cognitive impairment can be excluded from these opportunities when technology is not designed with their needs, preferences, and abilities in mind.
CAREER: API Can Code: Situating Computational Learning Opportunities in the Digital Lives of Students
Principal Investigator(s): David Weintrop
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Youth Experience, Learning, and Digital Practices
Principal Investigator(s): David Weintrop
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Youth Experience, Learning, and Digital Practices
CHS: Medium: Collaborative Research: Teachable Activity Trackers for Older Adults
Principal Investigator(s): Eun Kyoung Choe
Funders: 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
Funders: 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.
Collaborative Research: ER2: The development of research ethics governance projects in computer science
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics
This project characterizes and evaluates historical, ongoing, and emerging ethics governance projects within computer science. By creating a recent history of computing governance during this active period of questioning, the project will appraise and evaluate current efforts, and recommend best practices for computing research governance.
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Information Justice, Human Rights, and Technology Ethics
This project characterizes and evaluates historical, ongoing, and emerging ethics governance projects within computer science. By creating a recent history of computing governance during this active period of questioning, the project will appraise and evaluate current efforts, and recommend best practices for computing research governance.
Collaborative Research: Harmonizing Scratch Encore: Empowering Educators to Create Customized Culturally-Responsive Computing Materials
Principal Investigator(s): David Weintrop
Funders: National Science Foundation
Research Areas: Youth Experience, Learning, and Digital Practices
This project explores ways to support middle school computer science teachers in drawing on their students' cultural resources and prior knowledge to situate introductory computer science learning experiences.
Principal Investigator(s): David Weintrop
Funders: National Science Foundation
Research Areas: Youth Experience, Learning, and Digital Practices
This project explores ways to support middle school computer science teachers in drawing on their students' cultural resources and prior knowledge to situate introductory computer science learning experiences.
Collaborative Research: SaTC: CORE: Medium: Supporting Privacy Negotiation Among Multiple Stakeholders in Smart Environments
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity
Internet-of-Things devices are increasingly used in shared spaces (e.g., homes, apartments, schools, hospitals, workplaces), and different stakeholders in these environments have unique privacy needs and expectations. This project investigates privacy negotiation behaviors in smart environments by designing, developing, and deploying an interactive system to collect people’s real-world privacy negotiation behaviors.
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity
Internet-of-Things devices are increasingly used in shared spaces (e.g., homes, apartments, schools, hospitals, workplaces), and different stakeholders in these environments have unique privacy needs and expectations. This project investigates privacy negotiation behaviors in smart environments by designing, developing, and deploying an interactive system to collect people’s real-world privacy negotiation behaviors.
FAI: Advancing Deep Learning Towards Spatial Fairness
Principal Investigator(s): Sergii Skakun
Funders: 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
Funders: 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
Future of Interface and Accessibility Workshop
Principal Investigator(s): Gregg Vanderheiden
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design
This project is focused on looking at the past and future of interface and accessibility including the development of a 20 year R&D agenda
Principal Investigator(s): Gregg Vanderheiden
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design
This project is focused on looking at the past and future of interface and accessibility including the development of a 20 year R&D agenda
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
Funders: 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
Funders: 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.
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.