My research focuses on human factors in visualization, with a primary emphasis on the health and science domains. Specifically, I am interested in understanding how interaction, aesthetics, and storytelling strategies can impact the analysis and communication of complex information to different audiences and user groups.
I am also a professional biomedical artist, and worked for several years as an artist and content director in medical education start-ups in Silicon Valley, Chicago, and New York City. I love combining art, science, and technology to help people understand data or concepts, especially in the areas of biology and medicine. More recently, I’ve become interested in developing visualizations that are more accessible, i.e., usable and understandable, for different segments of the population.
I have a number of student projects available–if you are interested in working together, please reach out!
For more information on my research, feel free to browse my publications below or visit my personal website.
Publications
2025
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[Bibtex] @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/}
}@article{zhang2025deconstruct,
title={Deconstructing Implicit Beliefs in Visual Data Journalism: Unstable Meanings Behind Data as Truth & Design for Insight},
author={Zhang, Ke Er Amy and Jenkinson, Jodie and Garrison, Laura},
journal={arXiv, IEEE Transactions on Visualization and Computer Graphics--in press},
year={2025},
numpages={11},
publisher={arXiv},
doi = {10.48550/arXiv.2507.12377},
abstract={We conduct a deconstructive reading of a qualitative interview study with 17 visual data journalists from newsrooms across the globe. We borrow a deconstruction approach from literary critique to explore the instability of meaning in language and reveal implicit beliefs in words and ideas. Through our analysis we surface two sets of opposing implicit beliefs in visual data journalism: objectivity/subjectivity and humanism/mechanism. We contextualize these beliefs through a genealogical analysis, which brings deconstruction theory into practice by providing a historic backdrop for these opposing perspectives. Our analysis shows that these beliefs held within visual data journalism are not self-enclosed but rather a product of external societal forces and paradigm shifts over time. Through this work, we demonstrate how thinking with critical theories such as deconstruction and genealogy can reframe "success" in visual data storytelling and diversify visualization research outcomes. These efforts push the ways in which we as researchers produce domain knowledge to examine the sociotechnical issues of today's values towards datafication and data visualization. All supplemental materials for this work are available at osf.io/5fr48.},
pdf = {pdfs/zhang2025deconstruct.pdf},
images = {images/zhang2025deconstruct.png},
thumbnails = {images/zhang2025deconstruct_thumb.png},
project = {VIDI},
git={https://osf.io/5fr48/}
}@inproceedings{alhazwani2025datahum,
author = {Al-Hazwani, Ibrahim and Zhang, Ke Er Amy and Garrison, Laura and Bernard, J{\"u}rgen},
title = {Data Humanism decoded: A characterization of its principles to bridge
data visualization researchers and practitioners},
booktitle = {Proceedings of IEEE VIS 2025 (Short Papers)--in press"},
year = {2025},
numpages = {5},
publisher = {IEEE Computer Society},
address = {Los Alamitos},
abstract = {Data Humanism is a human-centered design approach that emphasizes the personal, contextual, and imperfect nature of data. Despite its growing influence among practitioners, the 13 principles outlined in Giorgia Lupi’s visual manifesto remain loosely defined in research contexts, creating a gap between design practice and systematic application. Through a mixed-methods approach, including a systematic literature review, multimedia analysis, and expert interviews, we present a characterization of Data Humanism principles for visualization researchers. Our characterization provides concrete definitions that maintain interpretive flexibility in operationalizing design choices. We validate our work through direct consultation with Lupi. Moreover, we leverage the characterization to decode a visualization work, mapping Data Humanism principles to specific visual design choices. Our work creates a common language for human-centered visualization, bridging the gap between practice and research for future applications and evaluations.},
pdf = {pdfs/alhazwani2025datahum.pdf},
images = {images/alhazwani2025datahum.png},
thumbnails = {images/alhazwani2025datahum_thumb.png},
}@article{mittenentzwei2025icom,
title = {AI-based character generation for disease stories: A case study using epidemiological data to highlight preventable risk factors},
author = {Mittenentzwei, Sarah and Garrison, Laura A. and Budich, Beatrice and Lawonn, Kai and Dockhorn, Alexander and Preim, Bernhard and Meuschke, Monique},
year = 2025,
journal = {i-com},
publisher = {De Gruyter},
pages = {},
doi = {10.1515/icom-2024-0041},
abstract = {Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output.},
images = {images/mittenentzwei2025icom.png},
thumbnails = {images/mittenentzwei2025icom_thumb.png},
pdf = {pdfs/mittenentzwei2025icom.pdf}
}@inproceedings{zhang2025stories,
author = {Zhang, Ke Er Amy and Garrison, Laura},
title = {Modern snapshots in the crafting of a medical illustration},
booktitle = {Proceedings of CHI '25 Workshop "How do design stories work? Exploring narrative forms of knowledge in HCI"},
year = {2025},
numpages = {3},
abstract = {The time-honored practice of medical illustration and visualization, has, like nearly all other disciplines, seen changes in its tooling and development pipeline in step with technological and societal developments. At its core, however, medical visualization remains a discipline focused on telling stories about biology and medicine. The story we tell in this work assumes a more distant vantage point to tell a story about the biomedical storytellers themselves. Our story peers over the shoulders of two medical illustrators in the middle of a project to illustrate a procedure in one of the small blood vessels around the heart, and through the medium of an online chat explores the dialogue, tensions, and goals of such projects in the digital age. We adopt the two-column format of the CHI template, as it is more reminiscent of the width of our usual messaging windows while working. The second part of our submission reflects on these tensions and modes of storytelling from an HCI and Visualization-situated perspective.},
pdf = {pdfs/zhang2025stories.pdf},
images = {images/zhang2025stories.png},
thumbnails = {images/zhang2025stories.png},
project = {VIDI}
}2024
[Bibtex] @inproceedings{correll2024bodydata,
abstract = {With changing attitudes around knowledge, medicine, art, and technology, the human body has become a source of information and, ultimately, shareable and analyzable data. Centuries of illustrations and visualizations of the body occur within particular historical, social, and political contexts. These contexts are enmeshed in different so-called data cultures: ways that data, knowledge, and information are conceptualized and collected, structured and shared. In this work, we explore how information about the body was collected as well as the circulation, impact, and persuasive force of the resulting images. We show how mindfulness of data cultural influences remain crucial for today's designers, researchers, and consumers of visualizations. We conclude with a call for the field to reflect on how visualizations are not timeless and contextless mirrors on objective data, but as much a product of our time and place as the visualizations of the past.},
author = {Correll, Michael and Garrison, Laura A.},
booktitle = {arXiv, Proc CHI24},
doi = {10.48550/arXiv.2402.05014},
publisher = {arXiv},
title = {When the Body Became Data: Historical Data Cultures and Anatomical Illustration},
year = {2024},
month = {Feb},
pdf = {pdfs/garrisonCHI24.pdf},
images = {images/garrisonCHI24.png},
thumbnails = {images/garrisonCHI24.png},
project = {VIDI}
}@article{pokojna2024language,
title={The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling},
author={Pokojn{\'a}, Hana and Isenberg, Tobias and Bruckner, Stefan and Kozl{\'i}kov{\'a}, Barbora and Garrison, Laura},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
month={Oct},
publisher={IEEE},
abstract={We apply an approach from cognitive linguistics by mapping Conceptual Metaphor Theory (CMT) to the visualization domain to address patterns of visual conceptual metaphors that are often used in science infographics. Metaphors play an essential part in visual communication and are frequently employed to explain complex concepts. However, their use is often based on intuition, rather than following a formal process. At present, we lack tools and language for understanding and describing metaphor use in visualization to the extent where taxonomy and grammar could guide the creation of visual components, e.g., infographics. Our classification of the visual conceptual mappings within scientific representations is based on the breakdown of visual components in existing scientific infographics. We demonstrate the development of this mapping through a detailed analysis of data collected from four domains (biomedicine, climate, space, and anthropology) that represent a diverse range of visual conceptual metaphors used in the visual communication of science. This work allows us to identify patterns of visual conceptual metaphor use within the domains, resolve ambiguities about why specific conceptual metaphors are used, and develop a better overall understanding of visual metaphor use in scientific infographics. Our analysis shows that ontological and orientational conceptual metaphors are the most widely applied to translate complex scientific concepts. To support our findings we developed a visual exploratory tool based on the collected database that places the individual infographics on a spatio-temporal scale and illustrates the breakdown of visual conceptual metaphors.},
pdf = {pdfs/garrisonVIS24.pdf},
images = {images/garrisonVIS24.png},
thumbnails = {images/garrisonVIS24thumb.png},
project = {VIDI},
git={https://osf.io/8xrjm/}
}@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/}
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