Healthcare and Accessibility
Extending quality diagnostics and personalized care to rural and underserved populations through equitable AI systems, while preserving privacy, patient autonomy, and provider autonomy.
Our Research Themes and Areas of Focus
Areas and Focus
Our Research Areas
Design and Interaction
Natural language systems with cultural nuance; multimodal interfaces; adaptive accessibility; affective computing
Data Science and Impact
Bias detection/mitigation; privacy-preserving methods; health equity; educational innovation; environmental monitoring
Robotics and Embodiment
Assistive technologies; collaborative manufacturing; social robotics; safety in human-robot teams
Ethics and Governance
Fairness auditing; accountability frameworks; policy research; inclusive AI design; environmental sustainability
Foundational Theory
Human-AI trust calibration; cultural factors in AI design; autonomy vs. assistance trade-offs; explanability-performance balance
Impact Areas
From Research to Real Solutions
Education and Opportunity
Creating adaptive learning systems that support diverse learning styles, identify at-risk students responsibly, and expand quality education access to remote communities.
Environmental Sustainability
Using data science and robotics to optimize renewable energy, predict climate impacts, monitor biodiversity, and support sustainable agriculture, while minimizing AI's environmental footprint.
Labor and Economic Justice
Researching how automation affects workers and designing AI systems that augment human capabilities, preserve dignified livelihoods, and protect vulnerable populations from displacement.
Social Justice and Democratic Participation
Developing tools that make algorithmic decisions transparent, contestable, and accountable, informing evidence-based policy while guarding against surveillance and manipulation.
Research Methodologies
Research That Listens, Learns, and Transforms
In this institution, we work together with a variety of stakeholders to understand the impact of and ways to address responsible integration of AI in human life. Through transdisciplinary methods, stakeholders are brought together in dialogue to talk about challenges and opportunities for sustainable solutions in various fields, including health, mental well-being, education, farming, or social justice.
- Employing stakeholder analysis
- Drawing up problem statements and research agendas with various stakeholders.
- Collecting and analyzing relevant data together
- Conducting scenario and outcome mapping sessions
- Inquiring into philosophical and ethical questions
- Conducting frame reflections
- Searching for policy- program, or innovation related opportunities
Transdisciplinary research means to engage in participatory and action-oriented methods towards a better future.