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.
An AI-Enhanced Colleague for Teachers: Developing and Studying an Innovative Platform for Efficient, Inclusive Middle-Grade Mathematics Lesson Planning
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project supports middle school math teachers by developing an AI-powered lesson planning tool that enhances efficiency, quality, and inclusivity. Integrating generative AI with research-based practices, it offers personalized guidance for creating effective lessons. The project also examines impacts on teacher stress, instructional effectiveness, and student learning outcomes.
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project supports middle school math teachers by developing an AI-powered lesson planning tool that enhances efficiency, quality, and inclusivity. Integrating generative AI with research-based practices, it offers personalized guidance for creating effective lessons. The project also examines impacts on teacher stress, instructional effectiveness, and student learning outcomes.
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
This project develops and studies a high school data science curriculum that integrates programming and real-world datasets to engage students in exploring their own questions and interests. Designed in partnership with an urban school district, the research focuses on expanding access to computing for populations historically excluded from the field.
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
This project develops and studies a high school data science curriculum that integrates programming and real-world datasets to engage students in exploring their own questions and interests. Designed in partnership with an urban school district, the research focuses on expanding access to computing for populations historically excluded from the field.
CAREER: Self-Directed Human-LLM Coordination for Language Learning and Information Seeking
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Principal Investigator(s): Ge Gao
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
CAREER: Socio-Algorithmic Foundations of Trustworthy Recommendations
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Social Networks, Online Communities, and Social Media
This project investigates how incorporating audience diversity into content recommendation systems can improve trustworthiness and news quality. It will develop new algorithms, evaluate re-ranking methods, and test impacts on the information diets of news consumers, particularly older audiences.
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Social Networks, Online Communities, and Social Media
This project investigates how incorporating audience diversity into content recommendation systems can improve trustworthiness and news quality. It will develop new algorithms, evaluate re-ranking methods, and test impacts on the information diets of news consumers, particularly older audiences.
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: Developing Educational Resources for the Ethical Use of Pervasive Data
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
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: Impacts of Hard/Soft Skills on STEM Workforce Trajectories
Principal Investigator(s): Christopher Antoun
Funders: National Science Foundation
This project conducts a longitudinal study to examine how communication, leadership, and management skills influence PhD graduates’ career trajectories across academic and non-academic sectors. By linking individual, program, and employer data, researchers will provide evidence-based insights to strengthen doctoral training and workforce preparation.
Principal Investigator(s): Christopher Antoun
Funders: National Science Foundation
This project conducts a longitudinal study to examine how communication, leadership, and management skills influence PhD graduates’ career trajectories across academic and non-academic sectors. By linking individual, program, and employer data, researchers will provide evidence-based insights to strengthen doctoral training and workforce preparation.