Research Projects
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.
CHS: Small: Collaborative Research: Making Information Deserts Visible: Computational Models, Disparities in Civic Technology Use, and Urban Decision Making
Principal Investigator(s):
Enhancing understanding of how civic technologies are used and how information inequalities manifest in a city by examining Boston's 311 system for reporting non-emergency issues to the city government and then using computational and qualitative approaches to identify, categorize, and understand the kinds of information disparities that are becoming institutionalized.
Principal Investigator(s):
Enhancing understanding of how civic technologies are used and how information inequalities manifest in a city by examining Boston's 311 system for reporting non-emergency issues to the city government and then using computational and qualitative approaches to identify, categorize, and understand the kinds of information disparities that are becoming institutionalized.
CHS: Small: Teachable Object Recognizers for the Blind
Principal Investigator(s): Hernisa Kacorri
Funder: National Science Foundation
The research aims to develop a teachable object recognizer (TOR) app for blind users, enabling them to train machine learning models with personalized data through their smartphone cameras. This "teachability" approach addresses data scarcity in assistive technology. The study will explore effective user training, measure system efficacy, and evaluate accessible interactions through various research methods, aiming to improve the robustness of assistive tech.
Principal Investigator(s): Hernisa Kacorri
Funder: National Science Foundation
The research aims to develop a teachable object recognizer (TOR) app for blind users, enabling them to train machine learning models with personalized data through their smartphone cameras. This "teachability" approach addresses data scarcity in assistive technology. The study will explore effective user training, measure system efficacy, and evaluate accessible interactions through various research methods, aiming to improve the robustness of assistive tech.
Collaborative Research: ER2: The development of research ethics governance projects in computer science
Principal Investigator(s): Katie Shilton
Funder: 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
Funder: 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
Funder: 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
Funder: 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
Funder: 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
Funder: 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.
Computational Thinking to Unlock the Japanese American WWII Camp Experience
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
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
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Connecting Rural and Small Libraries to Connected Learning
Principal Investigator(s): Mega Subramaniam
Funder: Institute of Museum and Library Services
Research Areas: Library and Information Science > Youth Experience, Learning, and Digital Practices
With funding from the Institute of Museum and Library Services (IMLS), the ConnectedLib Toolkit helps librarians incorporate digital media into their work with youth to promote connections across learning contexts. We are currently working with the Young Adult Library Services Association (YALSA) and the Association for Rural and Small Libraries (ARSL) to expand the toolkit to better meet the needs of library staff working in rural areas and small libraries.
Principal Investigator(s): Mega Subramaniam
Funder: Institute of Museum and Library Services
Research Areas: Library and Information Science > Youth Experience, Learning, and Digital Practices
With funding from the Institute of Museum and Library Services (IMLS), the ConnectedLib Toolkit helps librarians incorporate digital media into their work with youth to promote connections across learning contexts. We are currently working with the Young Adult Library Services Association (YALSA) and the Association for Rural and Small Libraries (ARSL) to expand the toolkit to better meet the needs of library staff working in rural areas and small libraries.
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
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
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.
Crowdsourced Data: Accuracy, Accessibility, Authority (CDAAA)
Principal Investigator(s): Victoria Van Hyning
Funder: Institute of Museum and Library Services
Research Areas: Accessibility and Inclusive Design > Digital Humanities > Information Justice, Human Rights, and Technology Ethics > Library and Information Science > Social Networks, Online Communities, and Social Media
CDAAA explores the sociotechnical barriers libraries, archives, and museums face in integrating crowdsourced transcriptions to discovery systems. Using data from surveys, semi-structured interviews, data integration demonstrations, and user testing with people who use screen readers, we will produce individualized LAM Partner Reports, a summative white paper, and open-access journal articles.
Principal Investigator(s): Victoria Van Hyning
Funder: Institute of Museum and Library Services
Research Areas: Accessibility and Inclusive Design > Digital Humanities > Information Justice, Human Rights, and Technology Ethics > Library and Information Science > Social Networks, Online Communities, and Social Media
CDAAA explores the sociotechnical barriers libraries, archives, and museums face in integrating crowdsourced transcriptions to discovery systems. Using data from surveys, semi-structured interviews, data integration demonstrations, and user testing with people who use screen readers, we will produce individualized LAM Partner Reports, a summative white paper, and open-access journal articles.