Understanding how people see, trust, and reason with data.
The MOCHA Lab studies visualization literacy, perception, and cognition — and how those human abilities meet AI. We build multi-agent and human-AI systems, measure trust and aesthetics in data visualization, and take this work into the hands of practitioners, Blind and Low Vision (BLV) communities, clinicians, and scientists.
What MOCHA stands for
Five lenses on the same question: how humans and AI systems make sense of visual data together.
Multi-agent
How AI agents, models, and humans coordinate and reason together over visual data.
Observational
Eye-tracking and behavioral studies of how people actually look at and scan visualizations.
Cognitive
The perceptual and cognitive mechanisms behind reading — and misreading — charts.
Human-AI
Trust and collaboration between people and AI/LLM systems in visual analytics.
Aesthetics
How style and design choices shape comprehension, engagement, and trust.
Research directions taking shape
From foundational studies of perception and cognition to real-world tools built with the people who'll use them.
Cognition & perception in visualization
How the mind processes, remembers, and sometimes misreads visual data.
Visualization & AI practitioners
Benchmarks, literacy tests, and design guidelines for the people and AI systems that build charts.
BLV & accessibility
Extending visualization literacy and perception research to blind and low-vision users.
Medical & genomic visualization
Visual tools for clinicians and researchers working with complex biomedical data.
Environment & data science
Applying visualization and interpolation methods to environmental and public-health data.
Let's brew something together.
I'm actively looking to collaborate — on visualization literacy, trust, aesthetics, human-AI teaming, or applications in accessibility, medicine, or environmental science. If any of this overlaps with your work, I'd love to hear from you.