Current research direction
Trustworthy Data Communication and Narrative Integrity
We investigate how data is miscommunicated, how problematic data-driven narratives can mislead people, and how design, visualization, and computational support can mitigate these effects. Our work unpacks the linguistic, visual, and analytical choices that shape how audiences interpret evidence, revealing where and why misunderstandings arise. By bridging empirical insight with intelligent authoring and verification tools, we aim to foster more faithful, transparent, and accountable forms of data communication—especially in journalism and other civic domains where information integrity is vital.
- verification
- misinformation
- uncertainty
- provenance
- explainability
- AI-assisted communication
Selected projects
Selected publications
- How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process (2026 · preprint)
- “The Data Says Otherwise” — Towards Automated Fact-checking and Communication of Data Claims (UIST 2024)
- More Than Data Stories: Broadening the Role of Visualization in Contemporary Journalism (TVCG 2024)
Methods & ways to contribute
Research methods
- Content and systematic analysis
- Controlled experiments and surveys
- Comparative expert studies
- Mixed-method evaluations
Relevant skills
- Uncertainty visualization
- Provenance surfacing and evidence presentation (text ↔ vis)
- Full-stack development (React, Flask, PostgreSQL)
- AI / LLM integration and API orchestration
- System prototyping and deployment
- Instrumentation and logging for user studies
- Evaluation and usability study design