Research Projects
Funder: National Science Foundation
NSF INCLUDES Alliance: Re-Imagining STEM Equity Utilizing Postdoctoral Pathways
Principal Investigator(s):
Funder: National Science Foundation
This project is funded by NSF Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (NSF INCLUDES), a comprehensive national initiative to enhance U.S. leadership in discoveries and innovations by focusing on diversity, inclusion and broadening participation in STEM at scale. The Alliance is co-funded by NSF's Alliances for Graduate Education and the Professoriate (AGEP) program, which supports institutional change to advance African American, Hispanic American, Native American Indian, Alaska Native, Native Hawaiian and Native Pacific Islander STEM doctoral candidates, postdoctoral research scholars and faculty toward tenure and promotion in academic institutions.
Principal Investigator(s):
Funder: National Science Foundation
This project is funded by NSF Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (NSF INCLUDES), a comprehensive national initiative to enhance U.S. leadership in discoveries and innovations by focusing on diversity, inclusion and broadening participation in STEM at scale. The Alliance is co-funded by NSF's Alliances for Graduate Education and the Professoriate (AGEP) program, which supports institutional change to advance African American, Hispanic American, Native American Indian, Alaska Native, Native Hawaiian and Native Pacific Islander STEM doctoral candidates, postdoctoral research scholars and faculty toward tenure and promotion in academic institutions.
Accessible Visualization for Blind Users
Principal Investigator(s): Jonathan Lazar
Funder: 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
Funder: 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
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.
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.
Future of Interface and Accessibility Workshop
Principal Investigator(s): Gregg Vanderheiden
Funder: 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
Funder: 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
NSF Convergence Accelerator Track J: MidAtlantic Food Resiliency Network – Securing the Future of Food through a Multi-Mindset Approach
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The Mid-Atlantic Food Resiliency Network (MFRN) aims to improve food security in the Mid-Atlantic, starting in Prince George’s County. Collaborating across multiple disciplines, the MFRN will develop tools and systems to reduce hunger, waste, and food deserts. Initiatives include understanding food behaviors, repurposing food waste, and training future food security leaders.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The Mid-Atlantic Food Resiliency Network (MFRN) aims to improve food security in the Mid-Atlantic, starting in Prince George’s County. Collaborating across multiple disciplines, the MFRN will develop tools and systems to reduce hunger, waste, and food deserts. Initiatives include understanding food behaviors, repurposing food waste, and training future food security leaders.
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.
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
CHS: Small: Teachable Object Recognizers for the Blind
Principal Investigator(s): Hernisa Kacorri
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
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
Research Areas: Accessibility and Inclusive Design > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
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.
PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling During Pandemics (PandEval)
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The PandEval project aims to enhance pandemic response by utilizing diverse data sources, including social media insights and real-life behavior tracking. It seeks to improve public health messaging and localized policies, with customized epidemiological models. The project's innovation lies in creating a Pand-Index, aiding individual decisions on measures like social distancing.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Health Informatics > Machine Learning, AI, Computational Linguistics, and Information Retrieval
The PandEval project aims to enhance pandemic response by utilizing diverse data sources, including social media insights and real-life behavior tracking. It seeks to improve public health messaging and localized policies, with customized epidemiological models. The project's innovation lies in creating a Pand-Index, aiding individual decisions on measures like social distancing.
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.
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.
Maryland Sports Data Analytics Camps for Youth
Principal Investigator(s): Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Advancing knowledge of informal learning experiences that build adolescents' motivation for participation in STEM courses and careers, with a specific focus on introducing middle school African American and Latinx youth to the world of sports data analytics through events and summer camps.
Principal Investigator(s): Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
Advancing knowledge of informal learning experiences that build adolescents' motivation for participation in STEM courses and careers, with a specific focus on introducing middle school African American and Latinx youth to the world of sports data analytics through events and summer camps.
Skill-XR: An Affordable and Scalable X-Reality (XR) Platform for Skills Training and Analytics in Manufacturing Workforce Education
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Online communities, from question and answer sites to general purpose discussion forums, are increasingly working to solve hard problems together, often through a process of open sharing and discussion of ideas and information.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval
Online communities, from question and answer sites to general purpose discussion forums, are increasingly working to solve hard problems together, often through a process of open sharing and discussion of ideas and information.
ScratchEncore: Equity via a Flexible, Culturally-Relevant Advanced Scratch Curriculum for Upper Elementary Diverse Students and Teachers
Principal Investigator(s): David Weintrop
Funder: National Science Foundation
Research Areas: Library and Information Science > Youth Experience, Learning, and Digital Practices
Principal Investigator(s): David Weintrop
Funder: National Science Foundation
Research Areas: Library and Information Science > Youth Experience, Learning, and Digital Practices
CAREER: Advancing Remote Collaboration: Inclusive Design for People with Dementia
Principal Investigator(s): Amanda Lazar
Funder: 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
Funder: 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.
CHS: Small: Collaborative Research: Toolkits for Aging in Place for Older Retirees (TAIPOR)
Principal Investigator(s): Amanda Lazar
Funder: National Science Foundation
Research Areas: Health Informatics > Human-Computer Interaction
Principal Investigator(s): Amanda Lazar
Funder: National Science Foundation
Research Areas: Health Informatics > Human-Computer Interaction
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.
Improving Data Discovery at the National Anthropological Archives: Pilot Study and National Survey
Principal Investigator(s): Diana E. Marsh
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Library and Information Science
Based on a three-year fellowship supported by the National Science Foundation, this research seeks to improve the discovery of anthropological archives for users. Current work includes exploring data reuse based on a national survey undertaken in 2018-2019 with the Association of Tribal Archives, Libraries, and Museums and the American Anthropological Association.
Principal Investigator(s): Diana E. Marsh
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Library and Information Science
Based on a three-year fellowship supported by the National Science Foundation, this research seeks to improve the discovery of anthropological archives for users. Current work includes exploring data reuse based on a national survey undertaken in 2018-2019 with the Association of Tribal Archives, Libraries, and Museums and the American Anthropological Association.
SCC-IRG Track 1: Inclusive Public Transit Toolkit to Assess Quality of Service Across Socioeconomic Status in Baltimore City
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.
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.
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.
SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait
Principal Investigator(s): Naeemul Hassan
Funder: 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
Funder: 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.
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 AGEP Alliance State System Model to Transform the Hiring Practices and Career Success of Tenure Track Historically Underrepresented Minority Faculty in Biomedical Sciences
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Health Informatics > Youth Experience, Learning, and Digital Practices
This collaborative research brings together five public universities with the goal of developing, implementing, studying, evaluating and disseminating a state level AGEP Alliance model to increase the number of historically underrepresented minority (URM) tenure-track faculty in the biomedical sciences.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Health Informatics > Youth Experience, Learning, and Digital Practices
This collaborative research brings together five public universities with the goal of developing, implementing, studying, evaluating and disseminating a state level AGEP Alliance model to increase the number of historically underrepresented minority (URM) tenure-track faculty in the biomedical sciences.
SaTC: EDU: Collaborative: Connecting Contexts: Building Foundational Digital Privacy and Security Skills for Elementary School Children, Teachers, and Parents
Principal Investigator(s): Jessica Vitak Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization > Human-Computer Interaction
Promoting elementary school children's privacy/cybersecurity learning across the two contexts where they spend most of their time, home and school, through the creation of curriculum and related educational materials tailored to grade level.
Principal Investigator(s): Jessica Vitak Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization > Human-Computer Interaction
Promoting elementary school children's privacy/cybersecurity learning across the two contexts where they spend most of their time, home and school, through the creation of curriculum and related educational materials tailored to grade level.
NRT-IGE: Information Infrastructure for Society: Integrating Data Science and Social Science in Graduate Education and Workforce Development
Principal Investigator(s): Brian Butler
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to the University of Maryland will pilot an innovative, cross-disciplinary curriculum that integrates Data Science with Social Science.
Principal Investigator(s): Brian Butler
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Youth Experience, Learning, and Digital Practices
This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to the University of Maryland will pilot an innovative, cross-disciplinary curriculum that integrates Data Science with Social Science.
III:Small:Safely Searching Among Sensitive Content
Principal Investigator(s): Douglas W. Oard Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Library and Information Science
Today's search engines are designed principally to help people find what they want to see. Paradoxically, the fact that search engines do this well means that there are many collections that can't be searched.
Principal Investigator(s): Douglas W. Oard Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Library and Information Science
Today's search engines are designed principally to help people find what they want to see. Paradoxically, the fact that search engines do this well means that there are many collections that can't be searched.
III: Small: DataWorld: Externalizing Hidden Data Flows for Anywhere Analytics
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Building an augmented-reality DataWorld using hidden troves of data (from social media, the census, public databases, and more) to help professionals, policymakers, and citizens in there every day life---from house hunting by walking through the neighborhood and getting pop-up facts about the area to getting event and safety updates as you walk through a college campus.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Building an augmented-reality DataWorld using hidden troves of data (from social media, the census, public databases, and more) to help professionals, policymakers, and citizens in there every day life---from house hunting by walking through the neighborhood and getting pop-up facts about the area to getting event and safety updates as you walk through a college campus.
Doctoral Consortium for ASSETS 2019
Principal Investigator(s): Amanda Lazar
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization
This is funding to support a Doctoral Consortium (workshop) of approximately 15 promising graduate students (up to 10 from the United States, who will be funded by this award), along with about 5 distinguished research faculty.
Principal Investigator(s): Amanda Lazar
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Data Science, Analytics, and Visualization
This is funding to support a Doctoral Consortium (workshop) of approximately 15 promising graduate students (up to 10 from the United States, who will be funded by this award), along with about 5 distinguished research faculty.
Collaborative Research: EAGER: Systems for Assisting in Emotion Regulation in the Wild
Principal Investigator(s): Keith Marzullo
Funder: National Science Foundation
Research Areas: Future of Work > Health Informatics > Human-Computer Interaction
Developing design guidelines for wearable technology that will aid people who need assistance with regulating emotions in the workplace and everyday life. The project is also exploring the impact of breathing interventions in real-world settings that were successful in the laboratory.
Principal Investigator(s): Keith Marzullo
Funder: National Science Foundation
Research Areas: Future of Work > Health Informatics > Human-Computer Interaction
Developing design guidelines for wearable technology that will aid people who need assistance with regulating emotions in the workplace and everyday life. The project is also exploring the impact of breathing interventions in real-world settings that were successful in the laboratory.
Collaboration in the Future of Work: Developing Playable Case Studies to Improve STEM Career Pathways
Principal Investigator(s): beth bonsignore
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Future of Work > Youth Experience, Learning, and Digital Practices
Developing role playing computer games for the classroom to encourage STEM learning. Students can collaborate with each other and fictional characters in an authentic scenario using a multimedia interface supported by chatbots, videoconferencing, and interactive STEM tools.
Principal Investigator(s): beth bonsignore
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Future of Work > Youth Experience, Learning, and Digital Practices
Developing role playing computer games for the classroom to encourage STEM learning. Students can collaborate with each other and fictional characters in an authentic scenario using a multimedia interface supported by chatbots, videoconferencing, and interactive STEM tools.
CCE STEM: Standard: Collaborative: The Development of Ethical Cultures in Computer Security Research
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Information Justice, Human Rights, and Technology Ethics
Examining how computer security researchers navigate ethical dilemmas when using big data and shared network resources to expose vulnerabilities - from ethical self-regulation to the sharing of ethics expectations in research communities.
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Information Justice, Human Rights, and Technology Ethics
Examining how computer security researchers navigate ethical dilemmas when using big data and shared network resources to expose vulnerabilities - from ethical self-regulation to the sharing of ethics expectations in research communities.
CCE STEM: Finding Practices that Cultivate Ethical Computing in Mobile and Wearable Application Research & Development
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Looking at mobile and wearable app development to discover factors that encourage discussion and action on ethical challenges amongst developers. Findings will be incorporated into curriculum for students in mobile app courses, and the impact on ethics education will be evaluated.
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Looking at mobile and wearable app development to discover factors that encourage discussion and action on ethical challenges amongst developers. Findings will be incorporated into curriculum for students in mobile app courses, and the impact on ethics education will be evaluated.
CAREER: Finding Levers for Privacy and Security by Design in Mobile Development
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Delving into why app developers have a low rate of prioritizing user data protection by looking at how developers define privacy and security, what would encourage them to prioritize data protection, and how can development tools encourage developers to implement more security features during design.
Principal Investigator(s): Katie Shilton
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Delving into why app developers have a low rate of prioritizing user data protection by looking at how developers define privacy and security, what would encourage them to prioritize data protection, and how can development tools encourage developers to implement more security features during design.
CAREER: Data-driven Models of Human Mobility and Resilience for Decision Making
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Future of Work
Using cell-phone data to better understand the reactions and movement of people in violent or disaster events, specifically looking at droughts in Haiti, armed conflicts in Colombia, and floods in Bangladesh, with the aim of providing decision makers with data-driven models they can use to create preparedness and response plans.
Principal Investigator(s): Vanessa Frias-Martinez
Funder: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity > Future of Work
Using cell-phone data to better understand the reactions and movement of people in violent or disaster events, specifically looking at droughts in Haiti, armed conflicts in Colombia, and floods in Bangladesh, with the aim of providing decision makers with data-driven models they can use to create preparedness and response plans.
AISL: Innovations in Development: Community-Driven Project That Adapt Technology for Environmental Learning in Nature Preserves
Principal Investigator(s): Jennifer J. Preece
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Social Networks, Online Communities, and Social Media > Youth Experience, Learning, and Digital Practices
Engaging members of low-income and minority communities in environmental projects that are meaningful to their lives and can promote informal STEM learning. The researchers will look at how these projects impact participants and identify the key factors that influence the development of such community projects.
Principal Investigator(s): Jennifer J. Preece
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics > Social Networks, Online Communities, and Social Media > Youth Experience, Learning, and Digital Practices
Engaging members of low-income and minority communities in environmental projects that are meaningful to their lives and can promote informal STEM learning. The researchers will look at how these projects impact participants and identify the key factors that influence the development of such community projects.
Adaptive Heads-up Displays for Simultaneous Interpretation
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design
Spearheading the development of new technology that will aid translators - providing automatic interpretation that is displayed for the translator and is particularly valuable for translating nuanced content.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design
Spearheading the development of new technology that will aid translators - providing automatic interpretation that is displayed for the translator and is particularly valuable for translating nuanced content.
Collaborative Research: Pervasive Data Ethics for Computational Research (PERVADE)
Principal Investigator(s): Katie Shilton Jessica Vitak
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Examining how the risks of using pervasive data in computational research can be quantified, what factors impact willingness to contribute data to research, how diverse computational researchers address core research ethics values, and how regulators are adjusting to the new burdens they face in governing computational research.
Principal Investigator(s): Katie Shilton Jessica Vitak
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Information Justice, Human Rights, and Technology Ethics
Examining how the risks of using pervasive data in computational research can be quantified, what factors impact willingness to contribute data to research, how diverse computational researchers address core research ethics values, and how regulators are adjusting to the new burdens they face in governing computational research.
CAREER: Ubilytics: Harnessing Existing Device Ecosystems for Anywhere Sensemaking
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Human-Computer Interaction > Smart Cities and Connected Communities
Proposing a comprehensive new approach called ubiquitous analytics (ubilytics) for harnessing ever-present digital devices into unified environments for anywhere analysis and sensemaking of data. Looking at applications for scientific discovery, classroom learning, and police investigation.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Human-Computer Interaction > Smart Cities and Connected Communities
Proposing a comprehensive new approach called ubiquitous analytics (ubilytics) for harnessing ever-present digital devices into unified environments for anywhere analysis and sensemaking of data. Looking at applications for scientific discovery, classroom learning, and police investigation.
NatureNet – Community-Driven Environmental Projects (C-DEP)
Principal Investigator(s): Jennifer J. Preece Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Human-Computer Interaction > Smart Cities and Connected Communities
The Community-Driven Environmental Projects (C-DEP) model is a multi-university research endeavor funded by the NSF AISL program with an objective to engage members of diverse communities in local nature and environmental conservation projects of their choosing.
Principal Investigator(s): Jennifer J. Preece Tamara Clegg
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Data Science, Analytics, and Visualization > Human-Computer Interaction > Smart Cities and Connected Communities
The Community-Driven Environmental Projects (C-DEP) model is a multi-university research endeavor funded by the NSF AISL program with an objective to engage members of diverse communities in local nature and environmental conservation projects of their choosing.
Advancing Personal Informatics through Semi-Automated Tracking
Principal Investigator(s): Eun Kyoung Choe
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Health Informatics > Human-Computer Interaction
Challenging the notion that fully automated health tracking tech is better for users, particularly older adults and surgical patients, since minimal personal tracking engagement is needed. This project examines semi-automated tracking, testing the hypothesis that some self-monitoring results in greater awareness of one's own health and data and better health/behavior outcomes.
Principal Investigator(s): Eun Kyoung Choe
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Health Informatics > Human-Computer Interaction
Challenging the notion that fully automated health tracking tech is better for users, particularly older adults and surgical patients, since minimal personal tracking engagement is needed. This project examines semi-automated tracking, testing the hypothesis that some self-monitoring results in greater awareness of one's own health and data and better health/behavior outcomes.
Data Analytics for Community Decision-Making
Principal Investigator(s): Susan Winter
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The project aims to find out how local communities can benefit from the advances in big data and data analytic technologies and how such technologies can create an innovation-supporting environment to stimulate economic growth in recovering communities.
Principal Investigator(s): Susan Winter
Funder: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization > Smart Cities and Connected Communities
The project aims to find out how local communities can benefit from the advances in big data and data analytic technologies and how such technologies can create an innovation-supporting environment to stimulate economic growth in recovering communities.