The AWARE Research Team (AWARE) focuses on developing technology to support awareness and collaboration across health and education ecosystems. We conduct research in human-computer interaction (HCI), accessibility, personal and health informatics, user experience, design, and human-centered artificial intelligence. Through participatory design, community-centered approaches, and user experience methods we design and evaulate technology in their applied contexts.
We aim to support people’s health and well-being by combining design, data, and user experiences to develop technological systems that enable awareness and collaboration across people and contexts.
Our Team
Current Members
RESEARCH AREAS
Human-Computer Interaction
Studying the design and use of interactive technologies across everyday contexts.
Personal & Health Informatics
Examining how personal data shapes health awareness and decision-making across people and contexts.
Accessibility
Designing inclusive technologies that support diverse abilities, needs, and experiences.
Human-Centered Artificial Intelligence
Crafting human-centered AI-integrated technology that align with human experiences and social life.
CURRENT PROJECTS
AudioBuddy – Supporting Awareness and Management of Noise Sensitivity
Effective support for sensory experiences can be enhanced by extending joint awareness to secondary supporters, such as teachers, coworkers, and healthcare professionals. We build on prior research to explore how digital health technologies can be designed to facilitate awareness and collaboration across broader community networks for sensory experiences. In particular, we investigate how to maintain person-centered care while enabling community-wide awareness and support by exploring how digital systems can combine informal support networks (eg, parents, teachers, peers, bystanders, etc.) with formal healthcare networks (eg, clinicians, healthcare institutions). This research explores how different types of supporters can be meaningfully integrated into collaborative collection and tracking of health data. This includes developing role-based interfaces that provide relevant information for different supporters, investigating how data consent, transparency, and sharing happen across care networks, and communication methods to facilitate coordinated care.
EarMark: Predicting and Supporting Noise Sensitivity Through Multi-Model Sensing
This project investigates how artificial intelligence and multimodal sensing can be used to understand, model, and predict sound-sensitivity experiences. We integrate psychophysiological and environmental data collected through consumer wearables and mobile devices to develop predictive models that characterize noise related trigger events from non-trigger states. The ultimate aim of this research is to advance personalized, AI powered just-in-time adaptive supports that anticipate trigger events and provide timely, context aware support for people who are noise sensitive and improve their well-being.