Amy Zhang

PhD Candidate

Critical Visualization, Visual Learning & Reasoning

 Team Garrison

I'm a scientifically-minded illustrator, designer, and visualization researcher. I contribute to projects that foster an appreciation and understanding of science within the VisGroup at the University of Bergen and VISABLI research network at the University of Toronto. Specifically, I am interested in the sociotechnical contexts of visualization as well as investigating visual representations as tools for learning and reasoning.

Prior to my PhD, I trained as a medical illustrator at the MSc Biomedical Communications program at the University of Toronto. After graduation, I worked as a researcher at the Science Visualization Lab, studying the use of visuals in undergraduate biology education. As well, I practiced as a policy analyst and designer within the Knowledge Translation division at the Public Health Agency of Canada. You can view a selection of my work at www.amykzhang.com.

Publications

2024

    [PDF] [Bibtex] 
    @MISC{zhang2024ManhattanWheel,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (Posters)},
title = {{The Manhattan Wheel: A Radial Visualization Story for Genome-wide Association Study Data}},
author = {Zhang, Ke Er and Vaudel, Marc and Garrison, Laura A.},
year = {2024},
howpublished = {Poster presented at VCBM 2024.},
publisher = {The Eurographics Association},
abstract = {Genome-wide association studies (GWAS) are critical to identifying genetic variations associated with a particular trait or disease. It is important to cultivate an awareness of GWAS in the general public as members of this group are key participants of these studies. However, low genetic data literacy and trust in the sharing of genetic data pose challenges to learning and engaging with GWAS concepts. In this design study, we explore design strategies for the public communication of GWAS data. As part of this study, we present an interactive visual prototype that explores the use of narrative structure, linked visualizations through scrollytelling, and plain language to onboard and communicate genetic concepts to a GWAS-naive audience.},
pdf = {pdfs/KEZHANG_VCBM2024_Poster_ManhattanWheel.pdf},
images = {images/zhangManhattanWheel.png},
thumbnails = {images/zhangManhattanWheelthumb.png},
project = {VIDI},
git={https://github.com/amykzhang/manhattan-wheel}
}