Research Overview
My research examines how people learn, participate, and exercise judgment in data-driven and AI-mediated systems. I focus on designing technologies that support expertise development, equitable participation, and responsible governance.
Augmented Expertise
How can we cultivate expertise among distributed, non-experts through adaptive systems?
I design systems that enable non-experts to develop specialized knowledge through participation in complex tasks. This work shows how interfaces, feedback, and structured workflows can transform novices into skilled contributors.
- Gravity Spy
- Language Socialization in Online Communities
- Learning routines in crowdsourced environments
- Human–AI co-learning in citizen science
Hybrid Intelligence Systems
How can humans and AI systems learn together?
This work explores systems where humans and machine learning models iteratively improve one another. Rather than treating humans as data labelers, I design systems that support mutual learning and evolving collaboration.
- Gravity Spy ML Integration
- Attention-based classification systems
- Co-learning in citizen science
- Machine learning for glitch classification
Algorithmic & Data Justice
How can data systems reflect community values?
I study how AI and data systems embed assumptions about fairness and legitimacy. This work develops participatory approaches where communities contribute to evaluating and shaping these systems.
- Participatory AI Auditing
- Public Sector AI Governance
- Beyond Bias Detection
- Public perceptions of data use
Civic Data & Participation
How can communities participate in data-driven decision-making?
This research focuses on inequities in data access and participation. I design systems that integrate community knowledge into environmental and civic decision-making processes.
- Environmental Justice Mapping
- The Knowledge Map
- Data, Place, and Perception
- Civic data participation studies