
PhD Candidate
HCI, emotion, health data visualization
- roxanne.ziman@uib.no
Supervised by: Laura Garrison
Visualization PhD student exploring the intersection of HCI, visual storytelling, and human health and medicine.
Teaching: INF358 Seminar in Visualization, Autumn 2025.
Publications
2025
![[PDF]](https://vis.uib.no/wp-content/plugins/papercite/img/pdf.png)
![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png)
@article{ziman2025genaixbiomedvis,
title={"It looks sexy but it's wrong." Tensions in creativity and accuracy using genAI for biomedical visualization},
author = {Ziman, Roxanne and Saharan, Shehryar and McGill, Ga\"{e}l and Garrison, Laura},
journal = {arXiv, IEEE Transactions on Visualization and Computer Graphics--in press},
year = {2025},
numpages = {11},
publisher = {arXiv},
doi = {10.48550/arXiv.2507.14494},
abstract = {We contribute an in-depth analysis of the workflows and tensions arising from generative AI (genAI) use in biomedical visualization (BioMedVis). Although genAI affords facile production of aesthetic visuals for biological and medical content, the architecture of these tools fundamentally limits the accuracy and trustworthiness of the depicted information, from imaginary (or fanciful) molecules to alien anatomy. Through 17 interviews with a diverse group of practitioners and researchers, we qualitatively analyze the concerns and values driving genAI (dis)use for the visual representation of spatially-oriented biomedical data. We find that BioMedVis experts, both in roles as developers and designers, use genAI tools at different stages of their daily workflows and hold attitudes ranging from enthusiastic adopters to skeptical avoiders of genAI. In contrasting the current use and perspectives on genAI observed in our study with predictions towards genAI in the visualization pipeline from prior work, our refocus the discussion of genAI's effects on projects in visualization in the here and now with its respective opportunities and pitfalls for future visualization research. At a time when public trust in science is in jeopardy, we are reminded to first do no harm, not just in biomedical visualization but in science communication more broadly. Our observations reaffirm the necessity of human intervention for empathetic design and assessment of accurate scientific visuals.},
pdf = {pdfs/ziman2025genaixbiomedvis.pdf},
images = {images/ziman2025itlookssexy.png},
thumbnails = {images/ziman2025itlookssexy_thumb.png},
project = {VIDI},
git = {https://osf.io/mbw86/}
}
2024
![[PDF]](https://vis.uib.no/wp-content/plugins/papercite/img/pdf.png)
![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png)
@inproceedings{zimanVCBM2024mobaDash,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Garrison, Laura and Jönsson, Daniel},
title = {{The MoBa Pregnancy and Child Development Dashboard: A Design Study}},
author = {Ziman, Roxanne and Budich, Beatrice and Vaudel, Marc and Garrison, Laura},
year = {2024},
month = {September},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-244-8},
DOI = {10.2312/vcbm.20241194},
abstract = {Visual analytics dashboards enable exploration of complex medical and genetic data to uncover underlying patterns and possible relationships between conditions and outcomes. In this interdisciplinary design study, we present a characterization of the domain and expert tasks for the exploratory analysis for a rare maternal disease in the context of the longitudinal Norwegian Mother, Father, and Child (MoBa) Cohort Study. We furthermore present a novel prototype dashboard, developed through an iterative design process and using the Python-based Streamlit App [TTK18] and Vega-Altair [VGH*18] visualization library, to allow domain experts (e.g., bioinformaticians, clinicians, statisticians) to explore possible correlations between women's health during pregnancy and child development outcomes. In conclusion, we reflect on several challenges and research opportunities for not only furthering this approach, but in visualization more broadly for large, complex, and sensitive patient datasets to support clinical research.},
pdf = {pdfs/zimanVCBM24.pdf},
images = {images/zimanVCBM24.png},
thumbnails = {images/zimanVCBM24thumb.png},
project = {VIDI},
git={https://osf.io/u6kdm/}
}